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  • 1
    Online Resource
    Online Resource
    Extending Power BI with Python and R : perform advanced analysis using the power of analytical languages (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837639533
    Language: English
    Pages: 1 online resource (814 pages) , illustrations
    Edition: Second edition.
    Series Statement: Expert insight
    DDC: 001.4/226028566
    Keywords: Information visualization Computer programs ; Visual analytics Data processing ; Data mining Computer programs ; Business intelligence Computer programs ; R (Computer program language) ; Python (Computer program language) ; Visualisation de l'information ; Logiciels ; Analyse visuelle ; Informatique ; Exploration de données (Informatique) ; Logiciels ; R (Langage de programmation) ; Python (Langage de programmation)
    Abstract: The latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python. This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis. You'll reinforce your learning with questions at the end of each chapter.
    Note: Includes bibliographical references and index
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  • 2
    Online Resource
    Online Resource
    ACTIVE MACHINE LEARNING WITH PYTHON : refine and elevate data quality over quantity with active learning (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781835462683 , 1835462685 , 9781835464946
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Apprentissage automatique ; Python (Langage de programmation) ; Exploration de données (Informatique)
    Abstract: Use active machine learning with Python to improve the accuracy of predictive models, streamline the data analysis process, and adapt to evolving data trends, fostering innovation and progress across diverse fields Key Features Learn how to implement a pipeline for optimal model creation from large datasets and at lower costs Gain profound insights within your data while achieving greater efficiency and speed Apply your knowledge to real-world use cases and solve complex ML problems Purchase of the print or Kindle book includes a free PDF eBook Book Description Building accurate machine learning models requires quality data--lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You'll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you'll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You'll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you'll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools. What you will learn Master the fundamentals of active machine learning Understand query strategies for optimal model training with minimal data Tackle class imbalance, concept drift, and other data challenges Evaluate and analyze active learning model performance Integrate active learning libraries into workflows effectively Optimize workflows for human labelers Explore the finest active learning tools available today Who this book is for Ideal for data scientists and ML engineers aiming to maximize model performance while minimizing costly data labeling, this book is your guide to optimizing ML workflows and prioritizing quality over quantity. Whether you're a technical practitioner or team lead, you'll benefit from the proven methods presented in this book to slash data requirements and iterate faster. Basic Python proficiency and familiarity with machine learning concepts such as datasets and convolutional neural networks is all you need to get started.
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  • 3
    Online Resource
    Online Resource
    DEEP LEARNING FOR TIME SERIES COOKBOOK : use PyTorch and Python recipes for forecasting, classification, and anomaly detection (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781805122739 , 1805122738 , 9781805129233
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 519.5/5
    Keywords: Time-series analysis Data processing ; Deep learning (Machine learning) ; Python (Computer program language) ; Série chronologique ; Informatique ; Apprentissage profond ; Python (Langage de programmation)
    Abstract: Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes Key Features Learn the fundamentals of time series analysis and how to model time series data using deep learning Explore the world of deep learning with PyTorch and build advanced deep neural networks Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection Purchase of the print or Kindle book includes a free PDF eBook Book Description Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You'll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you'll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem. What you will learn Grasp the core of time series analysis and unleash its power using Python Understand PyTorch and how to use it to build deep learning models Discover how to transform a time series for training transformers Understand how to deal with various time series characteristics Tackle forecasting problems, involving univariate or multivariate data Master time series classification with residual and convolutional neural networks Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs) Who this book is for If you're a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.
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  • 4
    Online Resource
    Online Resource
    DATA-CENTRIC MACHINE LEARNING WITH PYTHON : the ultimate guide to engineering and deploying high-quality models based on good data (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804612415 , 1804612413 , 9781804618127
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Apprentissage automatique ; Python (Langage de programmation) ; Exploration de données (Informatique)
    Abstract: Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book Description In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability. What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.
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  • 5
    Online Resource
    Online Resource
    ACCELERATE MODEL TRAINING WITH PYTORCH 2.X : build more accurate models by boosting the model training process (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781805121916 , 180512191X , 9781805120100
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 006.3/2
    Keywords: Neural networks (Computer science) ; Machine learning ; Python (Computer program language) ; Réseaux neuronaux (Informatique) ; Apprentissage automatique ; Python (Langage de programmation)
    Abstract: Dramatically accelerate the building process of complex models using PyTorch to extract the best performance from any computing environment Key Features Reduce the model-building time by applying optimization techniques and approaches Harness the computing power of multiple devices and machines to boost the training process Focus on model quality by quickly evaluating different model configurations Purchase of the print or Kindle book includes a free PDF eBook Book Description Penned by an expert in High-Performance Computing (HPC) with over 25 years of experience, this book is your guide to enhancing the performance of model training using PyTorch, one of the most widely adopted machine learning frameworks. You'll start by understanding how model complexity impacts training time before discovering distinct levels of performance tuning to expedite the training process. You'll also learn how to use a new PyTorch feature to compile the model and train it faster, alongside learning how to benefit from specialized libraries to optimize the training process on the CPU. As you progress, you'll gain insights into building an efficient data pipeline to keep accelerators occupied during the entire training execution and explore strategies for reducing model complexity and adopting mixed precision to minimize computing time and memory consumption. The book will get you acquainted with distributed training and show you how to use PyTorch to harness the computing power of multicore systems and multi-GPU environments available on single or multiple machines. By the end of this book, you'll be equipped with a suite of techniques, approaches, and strategies to speed up training , so you can focus on what really matters--building stunning models! What you will learn Compile the model to train it faster Use specialized libraries to optimize the training on the CPU Build a data pipeline to boost GPU execution Simplify the model through pruning and compression techniques Adopt automatic mixed precision without penalizing the model's accuracy Distribute the training step across multiple machines and devices Who this book is for This book is for intermediate-level data scientists who want to learn how to leverage PyTorch to speed up the training process of their machine learning models by employing a set of optimization strategies and techniques. To make the most of this book, familiarity with basic concepts of machine learning, PyTorch, and Python is essential. However, there is no obligation to have a prior understanding of distributed computing, accelerators, or multicore processors.
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  • 6
    Online Resource
    Online Resource
    Hands-on microservices with Django : build cloud-native and reactive applications with Python using Django 5 (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781835468524
    Language: English
    Pages: 1 online resource (278 pages) , illustrations
    Edition: First edition.
    DDC: 006.7/6
    Keywords: Django (Electronic resource) ; Python (Computer program language) ; Web site development ; Internet programming ; Python (Langage de programmation) ; Sites Web ; Développement ; Programmation Internet
    Abstract: Are you a Django developer looking to leverage microservices to create optimized and scalable web applications? If yes, then this book is for you. With microservices, you can split an application into self-contained services, each with a specific scope running asynchronously while collectively executing processes. Written by an experienced Python developer, Hands-On Microservices with Django teaches you how to develop and deploy microservices using Django and accompanying components such as Celery and Redis. You'll start by learning the principles of microservices and message/task queues and how to design them effectively. Next, you’ll focus on building your first microservices with Django RESTful APIs (DFR) and RabbitMQ, mastering the fundamentals along the way. As you progress, you’ll get to grips with dockerizing your microservices. Later, you’ll discover how to optimize and secure them for production environments. By the end of this book, you’ll have the skills you need to design and develop production-ready Django microservices applications with DFR, Celery/RabbitMQ, Redis, and Django's cache framework.
    Note: Includes index
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  • 7
    Online Resource
    Online Resource
    Interpretable machine learning with Python : build explainable, fair, and robust high-performance models with hands-on, real-world examples (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803235424
    Language: English
    Pages: 1 online resource (606 pages) , illustrations
    Edition: Second edition.
    Series Statement: Expert insight
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining
    Abstract: Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data. This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.
    Note: Includes bibliographical references and index
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  • 8
    Online Resource
    Online Resource
    APPLIED GEOSPATIAL DATA SCIENCE WITH PYTHON : leverage geospatial data analysis and modeling to find unique solutions to environmental problems (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803240343 , 1803240342 , 9781803238128
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 910.285
    Keywords: Geographic information systems Data processing ; Data mining ; Python (Computer program language)
    Abstract: Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book Description Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you'll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You'll learn how to read, process, and manipulate spatial data effectively. With data in hand, you'll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you'll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you'll be able to tackle random data, find meaningful correlations, and make geospatial data models. What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.
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  • 9
    Online Resource
    Online Resource
    The handbook of NLP with Gensim : leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803245508 , 1803245506 , 9781803244945
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 006.3/5
    Keywords: Natural language processing (Computer science) Software ; Python (Computer program language) ; Open source software ; Traitement automatique des langues naturelles ; Logiciels ; Python (Langage de programmation) ; Logiciels libres
    Abstract: Elevate your natural language processing skills with Gensim and become proficient in handling a wide range of NLP tasks and projects Key Features Advance your NLP skills with this comprehensive guide covering detailed explanations and code practices Build real-world topical modeling pipelines and fine-tune hyperparameters to deliver optimal results Adhere to the real-world industrial applications of topic modeling in medical, legal, and other fields Purchase of the print or Kindle book includes a free PDF eBook Book Description Navigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios. You'll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy. Next, you'll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you'll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications. By the end of this book, you'll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes. What you will learn Convert text into numerical values such as bag-of-word, TF-IDF, and word embedding Use various NLP techniques with Gensim, including Word2Vec, Doc2Vec, LSA, FastText, LDA, and Ensemble LDA Build topical modeling pipelines and visualize the results of topic models Implement text summarization for legal, clinical, or other documents Apply core NLP techniques in healthcare, finance, and e-commerce Create efficient chatbots by harnessing Gensim's NLP capabilities Who this book is for This book is for data scientists and professionals who want to become proficient in topic modeling with Gensim. NLP practitioners can use this book as a code reference, while students or those considering a career transition will find this a valuable resource for advancing in the field of NLP. This book contains real-world applications for biomedical, healthcare, legal, and operations, making it a helpful guide for project managers designing their own topic modeling applications.
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  • 10
    Online Resource
    Online Resource
    Machine learning with LightGBM and Python : a practitioner's guide to developing production-ready machine learning systems (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781800563056 , 1800563051 , 9781800564749
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining
    Abstract: Take your software to the next level and solve real-world data science problems by building production-ready machine learning solutions using LightGBM and Python Key Features Get started with LightGBM, a powerful gradient-boosting library for building ML solutions Apply data science processes to real-world problems through case studies Elevate your software by building machine learning solutions on scalable platforms Purchase of the print or Kindle book includes a free PDF eBook Book Description Machine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you'll explore the intricacies of gradient boosting machines and LightGBM. You'll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you'll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you'll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker. What you will learn Get an overview of ML and working with data and models in Python using scikit-learn Explore decision trees, ensemble learning, gradient boosting, DART, and GOSS Master LightGBM and apply it to classification and regression problems Tune and train your models using AutoML with FLAML and Optuna Build ML pipelines in Python to train and deploy models with secure and performant APIs Scale your solutions to production readiness with AWS Sagemaker, PostgresML, and Dask Who this book is for This book is for software engineers aspiring to be better machine learning engineers and data scientists unfamiliar with LightGBM, looking to gain in-depth knowledge of its libraries. Basic to intermediate Python programming knowledge is required to get started with the book. The book is also an excellent source for ML veterans, with a strong focus on ML engineering with up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML, and Dask.
    Note: Includes index
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  • 11
    Online Resource
    Online Resource
    Forecasting time series data with Prophet : build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837630417
    Language: English
    Pages: 1 online resource (282 pages) , illustrations
    Edition: Second edition.
    DDC: 519.5/5
    Keywords: Time-series analysis Data processing ; Python (Computer program language) ; Machine learning
    Abstract: Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python. Prophet empowers Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with high accuracy using only a few lines of code. You'll begin by exploring the evolution of time series forecasting, from basic early models to present-day advanced models. After the initial installation and setup, you'll take a deep dive into the mathematics and theory behind Prophet. You'll then cover advanced features such as visualizing your forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. This updated edition has a new section on modeling shocks such as COVID. Later on in the book you'll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and discover useful features when running Prophet in production environments. By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.
    Note: Includes index
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  • 12
    Online Resource
    Online Resource
    Flask framework cookbook : enhance your Flask skills with advanced techniques and build dynamic, responsive web applications (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804610008 , 1804610003 , 9781804611104
    Language: English
    Pages: 1 online resource (318 pages) , illustrations
    Edition: Third edition.
    Parallel Title: Erscheint auch als
    DDC: 005.3
    Keywords: Application software Development ; Web applications ; Python (Computer program language)
    Abstract: Design and deploy robust state-of-the-art web applications using Flask 2.x and Python 3 frameworks and libraries for streamlined development and optimal performance Purchase of the print or Kindle book includes a free PDF eBook Key Features A practical and rich companion guide for web developers, offering real-world situations and use cases to learn Flask Get the most out of the powerful Flask framework while preserving the flexibility of your design choices Write cleaner, testable, and maintainable code with the help of sample apps Book Description Discover what makes Flask, the lightweight Python web framework, popular, as you delve into its modular design that enables the development of scalable web apps. With this practical guide, you'll explore modern solutions, recommended design patterns, and best practices for Flask web development. Updated to the latest version of Flask and Python, this third edition of the Flask Framework Cookbook moves away from the outdated libraries, updates content to incorporate new coding patterns, and introduces recipes for the latest tools. You'll explore different ways to integrate with GPT to build AI-ready Flask applications. The book starts with an exploration of Flask application configurations and then guides you through working with templates and understanding the ORM and view layers. You'll also be able to write an admin interface and get to grips with testing using the factory pattern, debugging, and logging errors. Then you'll discover different ways of using Flask to create, deploy, and manage microservices using AWS, GCP, and Kubernetes. Finally, you'll gain insights into various deployment and post-deployment techniques for platforms such as Apache, Tornado, and Datadog. By the end of this book, you'll have acquired the knowledge necessary to write Flask applications that cater to a wide range of use cases in the best possible way and scale them using standard industry practices. What you will learn Explore advanced templating and data modeling techniques Discover effective debugging, logging, and error-handling techniques in Flask Work with different types of databases, including RDBMS and NoSQL Integrate Flask with different technologies such as Redis, Sentry, and Datadog Deploy and package Flask applications with Docker and Kubernetes Integrate GPT with your Flask application to build future-ready platforms Implement continuous integration and continuous deployment (CI/CD) to ensure efficient and consistent updates to your Flask web applications Who this book is for If you are a web developer seeking to expand your knowledge of developing scalable and production-ready applications in Flask, this is the book for you. It is also highly valuable if you are already aware of Flask's major extensions and want to leverage them for better application development. This book will come handy as a quick reference for specific topic on Flask, its popular extensions, or specific use cases. It assumes basic Python programming experience, as well as familiarity with web development and related terminology.
    Note: Includes index
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  • 13
    Online Resource
    Online Resource
    PRACTICAL COMPUTER ARCHITECTURE WITH PYTHON AND ARM : an introductory guide for enthusiasts and students to learn how computers work and program their own (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837632015 , 1837632014 , 9781837636679
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 004.2/2
    Keywords: Computer architecture ; Python (Computer program language) ; RISC microprocessors
    Abstract: Learn computer architecture with Python and ARM, simulating assembly program execution and designing a computer simulator Purchase of the print or Kindle book includes a free PDF eBook Key Features Build a computer simulator with Python: Learn computer architecture by designing and constructing a simulator Python for architecture: Use Python to simulate and execute assembly language instructions ARM programming on Raspberry Pi: Explore ARM assembly language and run programs on Raspberry Pi Book Description This comprehensive guide offers a unique and immersive learning experience by combining Python programming with ARM architecture. Starting with an introduction to computer architecture and the flow of data within a computer system, you'll progress to building your own interpreter using Python. You'll see how this foundation enables the simulation of computer operations and learn ways to enhance a simulator by adding new instructions and displaying improved results. As you advance, you'll explore the TC1 Assembler and Simulator Program to gain insights into instruction analysis and explore practical examples of simulators. This will help you build essential skills in understanding complex computer instructions, strengthening your grasp of computer architecture. Moreover, you'll be introduced to the Raspberry Pi operating system, preparing you to delve into the detailed language of the ARM computer. This includes exploring the ARM instruction set architecture, data-processing instructions, subroutines, and the stack. With clear explanations, practical examples, and coding exercises, this resource will enable you to design and construct your own computer simulator, simulate assembly language programs, and leverage the Raspberry Pi for ARM programming. What you will learn Master the core principles of computer architecture Understand the role of registers, memory, and data flow in computers Discover how to design and implement a computer simulator using Python Simulate and execute assembly language programs on the simulator Enhance the simulator using new instructions for improved output Analyze complex computer instructions for deeper architectural understanding Explore the ARM instruction set and data processing on the Raspberry Pi Develop proficiency in writing, assembling, and running ARM code on the Raspberry Pi Who this book is for This book is for university students studying computer science, particularly those enrolled in a computer architecture module. With its practical approach and succinct explanations, it is also suitable for hobbyists, enthusiasts, and self-learners seeking a deeper understanding of computer systems. The book assumes foundational knowledge of number bases, binary arithmetic, and Boolean logic concepts. While it primarily caters to the computer science field, this book is less geared toward electrical or electronics engineering.
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  • 14
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    Online Resource
    Debugging machine learning models with Python : develop high-performance, low-bias, and explainable machine learning and deep learning models (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781800201132 , 1800201133 , 9781800208582
    Language: English
    Pages: 1 online resource , illustrations
    Parallel Title: Erscheint auch als
    DDC: 005.1/4
    Keywords: Machine learning Computer simulation ; Debugging in computer science Computer programs ; Python (Computer program language)
    Abstract: Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies. By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.
    Note: Includes index
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  • 15
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    Online Resource
    A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON : elevate your machine learning projects with Networkx, PuLP, and linalg (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804617069 , 1804617067 , 9781804616703
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 006.3/1
    Keywords: Machine learning Mathematical models ; Python (Computer program language)
    Abstract: Master the art of mathematical modeling through practical examples, use cases, and machine learning techniques Key Features Gain a profound understanding of various mathematical models that can be integrated with machine learning Learn how to implement optimization algorithms to tune machine learning models Build optimal solutions for practical use cases Purchase of the print or Kindle book includes a free PDF eBook Book Description Mathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare. Through this book, you'll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you'll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning. Ultimately, you'll be able to apply everything you've learned to choose the most fitting methodologies for the business problems you encounter. What you will learn Understand core concepts of mathematical models and their relevance in solving problems Explore various approaches to modeling and learning using Python Work with tested mathematical tools to gather meaningful insights Blend mathematical modeling with machine learning to find optimal solutions to business problems Optimize ML models built with business data, apply them to understand their impact on the business, and address critical questions Apply mathematical optimization for data-scarce problems where the objective and constraints are known Who this book is for If you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.
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  • 16
    Online Resource
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    MODERN DATA ARCHITECTURES WITH PYTHON : a practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781801076418 , 1801076413 , 9781801070492
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Data structures (Computer science) ; Big data
    Abstract: Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features Develop modern data skills used in emerging technologies Learn pragmatic design methodologies such as Data Mesh and data lakehouses Gain a deeper understanding of data governance Purchase of the print or Kindle book includes a free PDF eBook Book Description Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You'll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You'll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you'll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you'll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you'll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you'll get hands-on experience with Apache Spark, one of the key data technologies in today's market. By the end of this book, you'll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems. What you will learn Understand data patterns including delta architecture Discover how to increase performance with Spark internals Find out how to design critical data diagrams Explore MLOps with tools such as AutoML and MLflow Get to grips with building data products in a data mesh Discover data governance and build confidence in your data Introduce data visualizations and dashboards into your data practice Who this book is for This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they're not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.
    Note: Includes index
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  • 17
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    Hands-on web scraping with Python : extract quality data from the web using effective Python techniques (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837636211
    Language: English
    Pages: 1 online resource (324 pages) , illustrations
    Edition: Second edition.
    DDC: 006.3/12
    Keywords: Data mining ; Python (Computer program language)
    Abstract: Web scraping is a powerful tool for extracting data from the web, but it can be daunting for those without a technical background. Designed for novices, this book will help you grasp the fundamentals of web scraping and Python programming, even if you have no prior experience. Adopting a practical, hands-on approach, this updated edition of Hands-On Web Scraping with Python uses real-world examples and exercises to explain key concepts. Starting with an introduction to web scraping fundamentals and Python programming, you’ll cover a range of scraping techniques, including requests, lxml, pyquery, Scrapy, and Beautiful Soup. You’ll also get to grips with advanced topics such as secure web handling, web APIs, Selenium for web scraping, PDF extraction, regex, data analysis, EDA reports, visualization, and machine learning. This book emphasizes the importance of learning by doing. Each chapter integrates examples that demonstrate practical techniques and related skills. By the end of this book, you’ll be equipped with the skills to extract data from websites, a solid understanding of web scraping and Python programming, and the confidence to use these skills in your projects for analysis, visualization, and information discovery.
    Note: Includes bibliographical references and index
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  • 18
    Online Resource
    Online Resource
    Learning geospatial analysis with Python : unleash the power of Python 3 with practical techniques for learning GIS and remote sensing (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837636853 , 1837636850 , 9781837639175
    Language: English
    Pages: 1 online resource (432 pages) , illustrations
    Edition: Fourth edition.
    DDC: 910.285
    Keywords: Geospatial data ; Python (Computer program language) ; Données géospatiales ; Python (Langage de programmation)
    Abstract: Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.
    Note: Includes bibliographical references and index
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  • 19
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    Online Resource
    Web development with Django : a definitive guide to building modern Python web applications using Django 4 (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803230603
    Language: English
    Pages: 1 online resource (764 pages) , illustrations
    Edition: Second edition.
    DDC: 006.7
    Keywords: Django (Electronic resource) ; Web site development ; Python (Computer program language) ; Electronic books
    Abstract: Do you want to develop reliable and secure applications that stand out from the crowd without spending hours on boilerplate code? You've made the right choice trusting the Django framework, and this book will tell you why. Often referred to as a “batteries included” web development framework, Django comes with all the core features needed to build a standalone application. Web Development with Django will take you through all the essential concepts and help you explore its power to build real-world applications using Python. Throughout the book, you'll get the grips with the major features of Django by building a website called Bookr – a repository for book reviews. This end-to-end case study is split into a series of bitesize projects presented as exercises and activities, allowing you to challenge yourself in an enjoyable and attainable way. As you advance, you'll acquire various practical skills, including how to serve static files to add CSS, JavaScript, and images to your application, how to implement forms to accept user input, and how to manage sessions to ensure a reliable user experience. You'll cover everyday tasks that are part of the development cycle of a real-world web application. By the end of this Django book, you'll have the skills and confidence to creatively develop and deploy your own projects.
    Note: Includes index
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  • 20
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    Machine learning engineering with Python : manage the lifecycle of machine learning models using MLOps with practical examples (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837631964
    Language: English
    Pages: 1 online resource (462 pages) , illustrations.
    Edition: Second edition.
    Series Statement: Expert insight
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Apprentissage automatique ; Python (Langage de programmation) ; Exploration de données (Informatique)
    Abstract: The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
    Note: Includes index
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  • 21
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    Online Resource
    Practical guide to applied conformal prediction in Python : learn and apply the best uncertainty frameworks to your industry applications (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781805120919 , 1805120913
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Python (Langage de programmation) ; Apprentissage automatique
    Abstract: "Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal Prediction Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications. Explore cutting-edge methods to measure and manage uncertainty in industry applications. The book will explain how Conformal Prediction differs from traditional machine learning. Book Description In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. ""Practical Guide to Applied Conformal Prediction in Python"" addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework set to revolutionize uncertainty management in various ML applications. Embark on a comprehensive journey through Conformal Prediction, exploring its fundamentals and practical applications in binary classification, regression, time series forecasting, imbalanced data, computer vision, and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. Practical examples in Python using real-world datasets reinforce intuitive explanations, ensuring you acquire a robust understanding of this modern framework for uncertainty quantification. This guide is a beacon for mastering Conformal Prediction in Python, providing a blend of theory and practical application. It serves as a comprehensive toolkit to enhance machine learning skills, catering to professionals from data scientists to ML engineers. What you will learn The fundamental concepts and principles of conformal prediction Learn how conformal prediction differs from traditional ML methods Apply real-world examples to your own industry applications Explore advanced topics - imbalanced data and multi-class CP Dive into the details of the conformal prediction framework Boost your career as a data scientist, ML engineer, or researcher Learn to apply conformal prediction to forecasting and NLP Who this book is for Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.".
    Note: Includes index. - Modern machine learning approaches. - Description based on online resource; title from digital title page (viewed on January 18, 2024)
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  • 22
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    Building data science applications with FastAPI : develop, manage, and deploy efficient machine learning applications with Python (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837637263 , 1837637261 , 9781837632749
    Language: English
    Pages: 1 online resource (422 pages) , illustrations
    Edition: Second edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Data mining
    Abstract: Learn all the features and best practices of FastAPI to build, deploy, and monitor powerful data science and AI apps, like object detection or image generation. Purchase of the print or Kindle book includes a free PDF eBook Key Features Uncover the secrets of FastAPI, including async I/O, type hinting, and dependency injection Learn to add authentication, authorization, and interaction with databases in a FastAPI backend Develop real-world projects using pre-trained AI models Book Description Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects - a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements. What you will learn Explore the basics of modern Python and async I/O programming Get to grips with basic and advanced concepts of the FastAPI framework Deploy a performant and reliable web backend for a data science application Integrate common Python data science libraries into a web backend Integrate an object detection algorithm into a FastAPI backend Build a distributed text-to-image AI system with Stable Diffusion Add metrics and logging and learn how to monitor them Who this book is for This book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
    Note: Includes index
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  • 23
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    Online Resource
    Hands-on application development with PyCharm : build applications like a pro with the ultimate Python development tool (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837632350
    Language: English
    Pages: 1 online resource (652 pages) , illustrations
    Edition: Second edition.
    DDC: 005
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming
    Abstract: In the quest to develop robust, professional-grade software with Python and meet tight deadlines, it’s crucial to have the best tools at your disposal. In this second edition of Hands-on Application Development with PyCharm, you’ll learn tips and tricks to work at a speed and proficiency previously reserved only for elite developers. To achieve that, you’ll be introduced to PyCharm, the premiere professional integrated development environment for Python programmers among the myriad of IDEs available. Regardless of how Python is utilized, whether for general automation scripting, utility creation, web applications, data analytics, machine learning, or business applications, PyCharm offers tooling that simplifies complex tasks and streamlines common ones. In this book, you'll find everything you need to harness PyCharm's full potential and make the most of Pycharm's productivity shortcuts. The book comprehensively covers topics ranging from installation and customization to web development, database management, and data analysis pipeline development helping you become proficient in Python application development in diverse domains. By the end of this book, you’ll have discovered the remarkable capabilities of PyCharm and how you can achieve a new level of capability and productivity. This book is for Python practitioners and learners looking to boost their productivity and proficiency by harnessing the features and capabilities of PyCharm, all while gaining insights into best practices for modern application development. Basic knowledge of Python is required, making the book accessible to both newcomers and experienced Python developers.
    Note: Includes index
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  • 24
    Online Resource
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    Building statistical models in Python : develop useful models for regression, classification, time series, and survival analysis (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804614280
    Language: English
    Pages: 1 online resource (420 pages) , illustrations
    Edition: 1st edition.
    DDC: 519.5
    Keywords: Mathematical statistics ; Mathematical models ; Python (Computer program language) ; Statistics
    Abstract: The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.
    Note: Includes bibliographical references and index
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  • 25
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    Online Resource
    Applied computational thinking with Python : algorithm design for complex real-world problems (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781837631087 , 1837631085 , 9781837632305
    Language: English
    Pages: 1 online resource (438 pages) , illustrations
    Edition: Second edition.
    DDC: 006.3/1
    Keywords: Computational learning theory ; Python (Computer program language) ; Théorie de l'apprentissage informatique ; Python (Langage de programmation)
    Abstract: Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
    Note: Includes index
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  • 26
    Online Resource
    Online Resource
    Full stack FastAPI, React, and MongoDB (2022)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803231822
    Language: English
    Pages: 1 online resource (336 pages) , illustrations
    Edition: [First edition].
    DDC: 005.1
    Keywords: Application program interfaces (Computer software) ; Application software Development ; JavaScript (Computer program language) ; Python (Computer program language) ; Electronic books
    Abstract: Wield the power of Python, React, and MongoDB to build web and data applications quickly and broaden your web development horizons. If you need to develop web applications quickly, where do you turn? Enter the FARM stack. The FARM stack combines the power of the Python ecosystem with REST and MongoDB and makes building web applications easy and fast. This book is a fast-paced, concise, and hands-on beginner's guide that will equip you with the skills you need to quickly build web applications by diving just deep enough into the intricacies of the stack's components. The book quickly introduces each element of the stack and then helps you merge them to build a medium-sized web application. You'll set up a document store with MongoDB, build a simple API with FastAPI, and create an application with React. Security is crucial on the web, so you'll learn about authentication and authorization with JSON Web Tokens. You'll also understand how to optimize images, cache responses with Redis, and add additional features to your application as well as explore tips, tricks, and best practices to make your development experience a breeze. Before you know it, you'll be deploying the application to different platforms. By the end of this book, you will have built a couple of functional applications efficiently and will have the springboard you need to delve into diverse and more specialized domains.
    Note: Includes index
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  • 27
    Online Resource
    Online Resource
    Deep learning with TensorFlow and Keras (2022)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803232911
    Language: English
    Pages: 1 online resource (698 pages) , illustrations
    Edition: Third edition.
    Series Statement: Expert insight
    DDC: 005.13/3
    Keywords: TensorFlow ; Machine learning ; Artificial intelligence ; Neural networks (Computer science) ; Python (Computer program language)
    Abstract: Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.
    Note: Includes bibliographical references and index
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  • 28
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    The Python workshop : write Python code to solve challenging real-world problems (2022)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804615805 , 1804615803 , 9781804610619
    Language: English
    Pages: 1 online resource (600 pages) , illustrations
    Edition: Second edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Computer program language) ; Electronic books
    Abstract: Python is among the most popular programming languages in the world. It's ideal for beginners because it's easy to read and write, and for developers, because it's widely available with a strong support community, extensive documentation, and phenomenal libraries – both built-in and user-contributed. This project-based course has been designed by a team of expert authors to get you up and running with Python. You'll work though engaging projects that'll enable you to leverage your newfound Python skills efficiently in technical jobs, personal projects, and job interviews. The book will help you gain an edge in data science, web development, and software development, preparing you to tackle real-world challenges in Python and pursue advanced topics on your own. Throughout the chapters, each component has been explicitly designed to engage and stimulate different parts of the brain so that you can retain and apply what you learn in the practical context with maximum impact. By completing the course from start to finish, you'll walk away feeling capable of tackling any real-world Python development problem.
    Note: Includes index
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    Bioinformatics with Python cookbook (2022)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803236421
    Language: English
    Pages: 1 online resource (360 pages) , illustrations
    Edition: Third edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Bioinformatics ; Computational biology
    Abstract: Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you'll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
    Note: Includes index
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  • 30
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    Python feature engineering cookbook (2022)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804611302
    Language: English
    Pages: 1 online resource (386 pages) , illustrations
    Edition: Second edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Application software Development ; Machine learning
    Abstract: Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.
    Note: Includes index
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    Hands-on data structures and algorithms with Python : store, manipulate, and access data effectively and boost the performance of your applications (2022)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781801073448
    Language: English
    Pages: 1 online resource (496 pages) , illustrations
    Edition: Third edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Electronic books ; Electronic books
    Abstract: Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You'll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer. By the end of this Python book, you'll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.
    Note: Includes index
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  • 32
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    Learn from the experts : Python (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (12 min.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Application software ; Electronic videos ; local
    Abstract: "In this Learn from the Experts interview, Jeff Bleiel talks to Yves Hilpisch about Python."--Resource description page.
    Note: Title from title screen (viewed April 4, 2018)
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  • 33
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    Mastering Python for networking and security : leverage Python scripts and libraries to overcome networking and security issues (2018)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781788990707 , 1788990706
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Master Python scripting to build a network and perform security operations Key Features Learn to handle cyber attacks with modern Python scripting Discover various Python libraries for building and securing your network Understand Python packages and libraries to secure your network infrastructure Book Description It's becoming more and more apparent that security is a critical aspect of IT infrastructure. A data breach is a major security incident, usually carried out by just hacking a simple network line. Increasing your network's security helps step up your defenses against cyber attacks. Meanwhile, Python is being used for increasingly advanced tasks, with the latest update introducing many new packages. This book focuses on leveraging these updated packages to build a secure network with the help of Python scripting. This book covers topics from building a network to the different procedures you need to follow to secure it. You'll first be introduced to different packages and libraries, before moving on to different ways to build a network with the help of Python scripting. Later, you will learn how to check a network's vulnerability using Python security scripting, and understand how to check vulnerabilities in your network. As you progress through the chapters, you will also learn how to achieve endpoint protection by leveraging Python packages along with writing forensic scripts. By the end of this book, you will be able to get the most out of the Python language to build secure and robust networks that are resilient to attacks. What you will learn Develop Python scripts for automating security and pentesting tasks Discover the Python standard library's main modules used for performing security-related tasks Automate analytical tasks and the extraction of information from servers Explore processes for detecting and exploiting vulnerabilities in servers Use network software for Python programming Perform server scripting and port scanning with Python Identify vulnerabilities in web applications with Python Use Python to extract metadata and forensics Who this book is for This book is ideal for network engineers, system administrators, or any security professional looking at tackling networking and security challenges. Programmers with some prior experience in Python will get the most out of this book. Some basic understanding of general programming structures and Python is required. Downloading the example code for this book You can dow...
    Note: Description based on online resource; title from title page (viewed November 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788992510/?ar
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  • 34
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    Online Resource
    Learn from the experts : Python (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (13 min., 47 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Electronic videos ; local
    Abstract: "In this 5 Questions interview, Jeff Bleiel talks to Aaron Maxwell about Python."--Resource description page.
    Note: Title from title screen (viewed April 4, 2018)
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  • 35
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    5 questions on Python (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (13 min., 17 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Electronic videos ; local
    Abstract: "In this 5 Questions interview, Jeff Bleiel talks to Chris Moffitt about Python."--Resource description page.
    Note: Title from title screen (viewed February 15, 2018). - Date of publication from resource description page
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  • 36
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    5 questions on Python (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (11 min., 35 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Electronic videos ; local
    Abstract: "In this 5 Questions interview, Jeff Bleiel talks with Luciano Ramalho about Python."--Resource description page.
    Note: Title from title screen (viewed March 13, 2018). - Date of publication from resource description page
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  • 37
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    Introduction to deep learning using PyTorch : create simple neural networks in Python using PyTorch (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 27 min.)) , digital, sound, color
    Keywords: Neural networks (Computer science) ; Python (Computer program language) ; Artificial intelligence ; Machine learning ; Electronic videos ; local
    Abstract: "This video will serve as an introduction to PyTorch, a dynamic, deep learning framework in Python. In this video, you will learn to create simple neural networks, which are the backbone of artificial intelligence. We will start with fundamental concepts of deep learning (including feed forward networks, back-propagation, loss functions, etc.) and then dive into using PyTorch tensors to easily create our networks. Finally, we will CUDA render our code in order to be GPU-compatible for even faster model training."--Resource description page.
    Note: Title from title screen (Safari, viewed February 22, 2018). - Release date from resource description page (Safari, viewed February 22, 2018). - Includes bibliographical references and index
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  • 38
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    Mastering predictive analytics with scikit-learn and TensorFlow : implement machine learning techniques to build advanced predictive models using Python (2018)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781789612240 , 1789612241
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data mining ; Big data ; Decision making ; Data processing ; Application software ; Development ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Learn advanced techniques to improve the performance and quality of your predictive models Key Features Use ensemble methods to improve the performance of predictive analytics models Implement feature selection, dimensionality reduction, and cross-validation techniques Develop neural network models and master the basics of deep learning Book Description Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems. This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics. By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis. What you will learn Use ensemble algorithms to obtain accurate predictions Apply dimensionality reduction techniques to combine features and build better models Choose the optimal hyperparameters using cross-validation Implement different techniques to solve current challenges in the predictive analytics domain Understand various elements of deep neural network (DNN) models Implement neural networks to solve both classification and regression problems Who this book is for Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.
    Note: Description based on online resource; title from title page (viewed November 6, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789617740/?ar
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  • 39
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    Image analysis and text classification using CNNs in PyTorch : learn to build powerful image and document classifiers in minutes (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (54 min., 31 sec.)) , digital, sound, color
    Keywords: Neural networks (Computer science) ; Python (Computer program language) ; Artificial intelligence ; Machine learning ; Electronic videos ; local
    Abstract: "This video teaches you how to build a powerful image classifier in just minutes using convolutional neural networks and PyTorch. It reviews the fundamental concepts of convolution and image analysis; shows you how to create a simple convolutional neural network (CNN) with PyTorch; and demonstrates how using transfer learning with a deep CNN to train on image datasets can generate state-of-the-art performance. The course is designed for the software engineer looking to get started with deep learning and for the AI researcher with TensorFlow or Theano experience who wants a smooth transition into PyTorch. Prerequisites include an understanding of algebra, basic calculus, and basic Python skills. Learners should download and install PyTorch before starting class."--Resource description page.
    Note: Title from title screen (viewed June 6, 2018)
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  • 40
    Online Resource
    Online Resource
    Learn from the experts : Python (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (11 min., 54 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Application software ; Electronic videos ; local
    Abstract: "In this 5 Questions interview, Jeff Bleiel talks with Katharine Jarmul about Python."--Resource description page.
    Note: Title from title screen (viewed April 4, 2018)
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  • 41
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    What are f-strings in Python and how can I use them? (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (8 min., 13 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Computer software ; Development ; Electronic videos ; local
    Abstract: "In Python 3.6, a new method of formatting strings was introduced - f-strings. In this video, Aaron Maxwell covers what f-strings are and how you can work with them for common string formatting tasks. Aaron takes you through progressively more complex examples of using f-strings to output your text. For developers working with Python 3.6 and higher, or planning to in the future, it is crucial to understand how f-strings compare to percent formatting and the str.format() method."--Resource description page.
    Note: Title from title screen (Safari, viewed July 7, 2017)
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  • 42
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    Analyzing data using Spark 2.0 DataFrames with Python : learning the basics of Python and Spark DataFrames, and how they work with big data (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 17 min., 29 sec.)) , digital, sound, color
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Big data ; Python (Computer program language) ; Data mining ; Electronic videos ; local
    Abstract: "Apache Spark 2.0 has become the gold standard for processing large datasets. This course, designed for learners with basic Python programming experience, takes you on an introductory journey into the world of big data analysis using Spark 2.0, Python, and the Spark DataFrame API. Beginning with an overview of Spark 2.0 and Python, and then moving into a detailed examination of DataFrames, you'll learn about using SQL with DataFrames, DataFrame dates and timestamps, DataFrame aggregate operations, and about DataFrames and missing data. The course includes a hands-on data analysis exercise using real stock data. Learners should have Python and Spark installed on their computers before starting the class."--Resource description page.
    Note: Title from title screen (viewed June 9, 2017). - Date of publication from resource description page
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  • 43
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    Mining the social web, mailboxes : learn to analyze and query large volumes of email using Python and Pandas (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (23 min., 37 sec.)) , digital, sound, color
    Keywords: Data mining ; Electronic mail messages ; Computer crimes ; Investigation ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "Imagine yourself as a criminal investigator. You've been tasked with searching through thousands of subpoenaed email messages for the purpose of finding evidence of fraud. What tools could you use to do your job? In this course, based on content from Matthew Russell's book, 'Mining the Social Web' (O'Reilly Media), you'll learn how to forensically examine large email data sets. Designed for learners with basic Python experience, the course explains the structure of email messages, deciphers the meanings in email metadata, and shows you how to use pandas -- Python's data analysis library -- to organize, manipulate, and query email data. Bonus: You get to practice your detective skills on an email data set used in a real U.S. criminal investigation (i.e., the 2001 Enron fraud case)."--Resource description page.
    Note: Title from title screen (viewed October 3, 2017). - Date of publication from resource description page
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  • 44
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    Mining the social web : Twitter (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (40 min., 35 sec.)) , digital, sound, color
    Keywords: Twitter ; Data mining ; Online social networks ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "Interested in tapping into Twitter data so you can discover what's trending, what people are talking about, and what feelings are being expressed in people's tweets? This course teaches you how to use a powerful set of tools that will allow you to acquire, analyze, and summarize Twitter data. You'll learn the meanings within Twitter's metadata, explore the data mining techniques of frequency analysis and sentiment, and gain experience using Python as a data mining tool. Learners should be familiar with Jupyter Notebooks and be able to install Python packages on their own using the command line."--Resource description page.
    Note: Title from title screen (viewed June 14, 2017). - Date of publication from resource description page
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  • 45
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    How do I handle missing dictionary keys in Python? (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (9 min., 6 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Computer software ; Development ; Electronic videos ; local
    Abstract: "When working with Python dictionaries, sometimes your code has to deal with keys that may not be there; depending on the situation, this is trickier than it looks. In fact, your code may have a major bug in it right now! Learn the three different techniques for handling missing dict keys, their different strengths, and avoidable gotchas."--Resource description page.
    Note: Title from title screen (Safari, viewed July 7, 2017)
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    Natural language text processing with Python : hands-on NLP in Python using NLTK, spaCy, gensim, and scikit-learn (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 54 min., 13 sec.)) , digital, sound, color
    Keywords: Natural language processing (Computer science) ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "Even though computers can't read, they're very effective at extracting information from natural language text. They can determine the main themes in the text, figure out if the writers of the text have positive or negative feelings about what they've written, decide if two documents are similar, add labels to documents, and more. This course shows you how to accomplish some common NLP (natural language processing) tasks using Python, an easy to understand, general programming language, in conjunction with the Python NLP libraries, NLTK, spaCy, gensim, and scikit-learn. The course is designed for basic level programmers with or without Python experience."--Resource description page.
    Note: Title from title screen (viewed February 1, 2017). - Date of publication from resource description page
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  • 47
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    Mining the social web, Google+ : learn to mine data from a unique social network built around interest-based communities (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (39 min., 52 sec.)) , digital, sound, color
    Keywords: Google+ (Electronic resource) ; Python (Computer program language) ; Data mining ; Natural language processing (Computer science) ; Application program interfaces (Computer software) ; Online social networks ; Electronic videos ; local
    Abstract: "Google+ is a social media platform that allows its users to create profile pages and submit posts to interest-based communities. It's become a useful platform for data mining because it organizes user posts according to topic, making it possible to obtain a lot of sample data on any given subject. This course, based on the book "Mining the Social Web" (O'Reilly Media) by Matthew Russell, teaches you how to mine Google+. You'll learn how to access Google+, download public posts, extract and parse text, and analyze the similarity of documents using natural language processing (NLP) techniques and the Python Natural Language Toolkit (NLTK). Learners should have a Google account and a basic understanding of Python."--Resource description page.
    Note: Title from title screen (viewed October 5, 2017). - Date of publication from resource description page
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  • 48
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    Mining the social web, GitHub : using Python and NetworkX to gain insight into GitHub's usage and community of users (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (33 min., 10 sec.)) , digital, sound, color
    Keywords: Computer software ; Development ; Open source software ; Data mining ; Python (Computer program language) ; Online social networks ; Electronic videos ; local
    Abstract: "GitHub is one of the largest social coding platforms on the web. Its collaborative features allow GitHub users to follow each other's code developments, build off each other's work, and make it easier than ever to create open source software. Based on content from the book "Mining the Social Web" (O'Reilly Media) by Matthew Russell, this course shows you how to mine GitHub data for insight into the platform's projects and community of users. For example, you'll be able to trace open source project histories, the types of programming languages used in those projects, and the relative popularity of those languages. The course teaches you how to make API requests on GitHub's developer platform, use NetworkX to construct interest graphs from GitHub data, and create visualizations of graphs. To get the most out of the course, learners should have basic Python experience and a GitHub account."--Resource description page.
    Note: Title from title screen (viewed October 5, 2017). - Date of publication from resource description page
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    Mining the social web, Facebook : learn how to explore and analyze Facebook data with Python and Facebook's Graph API (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (34 min., 43 sec.)) , digital, sound, color
    Keywords: Facebook (Electronic resource) ; Data mining ; Social media ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "Are you interested in exploring the data generated by Facebook's over 1.28 billion daily active users? Do you have some basic experience working with Python? If so, this course is for you. You'll explore Facebook's social graph and learn how it structures data; as well as discover how to use Python and Facebook's Graph API to connect to and query the social graph for page and user data, and pick up some experience manipulating and visualizing Facebook data using the powerful Python libraries, pandas and matplotlib. The course is taught by data scientist Mikhail Klassen and is based on content from Matthew Russell's book, 'Mining the Social Web' (O'Reilly Media)."--Resource description page.
    Note: Title from title screen (viewed June 26, 2017). - Date of publication from resource description page
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    Intermediate Python programming : tools and tips for developers (2016)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781491954935
    Language: English
    Pages: 1 online resource (1 streaming video file (2 hr., 51 min., 49 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Internet programming ; Computer networks ; Design and construction ; Open source software ; Electronic videos ; local
    Abstract: "What do Scrabble cheaters, Shakespearean sonnets, Twitter, and the Astronomy Picture of the Day have to do with Python software programming? For software impresario Jessica McKellar, they're playful tools for teaching intermediate-level Python programming techniques. During her time as an organizer of the Boston Python User Group (one of the world's largest Python groups), she taught hundreds of Python wannabees to happily program like pros. She uses that experience to benefit the next generation of Pythonistas in this follow-up to her widely popular Introduction to Python video."--Resource description page.
    Note: Title from title screen (viewed August 10, 2016)
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    Reactive Python for data science : push-based data analysis with RxPy (2016)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (2 hr., 17 min., 35 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Data mining ; Electronic videos ; local
    Abstract: "Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a dozen major languages and platforms. In this course, you'll learn to use RxPy, a lightweight Rx library, in Python data analysis workflows. It's designed for basic Python users who want to move beyond ad hoc data analysis and make their code geared toward a production environment, as well as for programmers familiar with Scala, Java 8, C#, Swift, and Kotlin who are interested in using the modern higher-order functional chain patterns from those languages."--Resource description page.
    Note: Title from title screen (viewed February 1, 2017)
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    Online Resource
    Online Resource
    Working with Excel spreadsheets in Python (2016)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Microsoft Excel (Computer file) ; Python (Computer program language) ; Electronic spreadsheets ; Electronic books ; Electronic books ; local
    Abstract: "Learn python for data analysis" is a constant refrain for those looking to develop their data analysis skills. But it's hard to apply beginning Python skills to your everyday work if the data you deal with is in Excel spreadsheets. This lesson gives you the ability to connect your new skills to the data you're already working with, and gives you a big step up on the Python learning curve. What you'll learn-and how you can apply it This lesson teaches you to ingest Excel spreadsheet files into Python scripts and begin to leverage Python's powerful analysis and automation tools, using both base Python and pandas. This lesson is for you because... You deal with data in spreadsheets as part of your job and are beginning to learn Python; this chapter gives you the ability to start applying your Python knowledge right away. Prerequisites Beginning Python knowledge Familiarity with Excel Materials or downloads needed in advance Python 3.x
    Note: Date of publication from resource description page. - "From Foundations for analytics with Python by Clinton W. Brownley"--Cover. - Description based on online resource; title from title page (Safari, viewed January 11, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491980026/?ar
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  • 53
    Online Resource
    Online Resource
    Building data pipelines with Python : understanding pipeline frameworks, workflow automation, and Python toolsets (2016)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (3 hr., 40 min., 56 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Data mining ; Electronic videos ; local
    Abstract: "This course shows you how to build data pipelines and automate workflows using Python 3. From simple task-based messaging queues to complex frameworks like Luigi and Airflow, the course delivers the essential knowledge you need to develop your own automation solutions. You'll learn the architecture basics, and receive an introduction to a wide variety of the most popular frameworks and tools. Designed for the working data professional who is new to the world of data pipelines and distributed solutions, the course requires intermediate level Python experience and the ability to manage your own system set-ups."--Resource description page.
    Note: Title from resource description page (viewed December 20, 2016)
    URL: lizenzpflichtig
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  • 54
    Online Resource
    Online Resource
    Making classes support comparison operations in Python (2016)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Keywords: Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: This lesson is a recipe from the Python Cookbook that explores being able to compare instances of your class using the standard comparison operators, but without having to write a lot of special methods, and avoiding the tedious task of creating implementations of every possible comparison operator. What you'll learn-and how you can apply it Learn how to use the functools.total_ordering decorator to simplify the comparison operator creation process. This lesson is for you because... You're an experienced Python programmer who wants to compare instances of your class using standard comparison operators without having to write a lot of special methods. Prerequisites Moderate experience with Python Materials or downloads needed None
    Note: Description based on online resource; title from cover (viewed February 20, 2017). - From Python cookbook, third edition, by Brian K. Jones and David Beazley. Cf. Cover. - Date of publication from resource description page
    URL: https://learning.oreilly.com/library/view/-/9781491965276/?ar
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  • 55
    Online Resource
    Online Resource
    Introduction to PySpark : from transformations and actions to performance--using the Spark Python API (2016)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771375535
    Language: English
    Pages: 1 online resource (1 streaming video file (48 min., 23 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; SPARK (Computer program language) ; Electronic videos ; local
    Abstract: "With Early Release video training, you get videos as they're being developed so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters as they're edited, and the final video. In this Introduction to PySpark training course, expert author Alex Robbins will teach you everything you need to know about the Spark Python API. This course is designed for users that already have a basic working knowledge of Python. You will start by learning how to install Spark, then jump into learning the Spark fundamentals. From there, Alex will teach you about transformations, including filter, pipe, repartition, and distinct. This video tutorial also covers actions, input and output, performance, and running on a cluster. Finally, you will learn advanced topics, including Spark streaming, dataframes and SQL, and MLlib. Once you have completed this computer based training course, you will have learned everything you need to know about PySpark. Working files are included, allowing you to follow along with the author throughout the lessons. "--Resource description page.
    Note: Title from title screen (viewed December 29, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 56
    Online Resource
    Online Resource
    Introduction to Pandas for developers : understand the basic workflows and gotchas of crawling, munging and plotting data (2016)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771375764
    Language: English
    Pages: 1 online resource (1 streaming video file (5 hr., 35 min., 43 sec.)) , digital, sound, color
    Keywords: Data mining ; Python (Computer program language) ; Machine learning ; Electronic videos ; local
    Abstract: "In this Introduction to Pandas for Developers training course, expert author Matt Harrison will teach you how to load data in Pandas data structures. This course is designed for users that are already familiar with Python. You will start by learning about Python lists, Pandas series, and boolean arrays, then jump into learning about dataframes. From there, Matt will teach you about relational algebra, tweaking dataframes, and joins. This video tutorial also covers filtering dataframes, grouping, serialization, plotting, and time series. Finally, you will learn about machine learning and using Pandas with scikit-learn. Once you have completed this computer based training course, you will have learned about basic workflows and gotchas of crawling, munging, and plotting data."--Resource description page.
    Note: Title from title screen (viewed April 1, 2016). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 57
    Online Resource
    Online Resource
    Learning Cython : combine the speed of C programs with the ease of Python coding (2016)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771375610
    Language: English
    Pages: 1 online resource (1 streaming video file (5 hr., 35 min., 39 sec.)) , digital, sound, color
    Keywords: Programming languages (Electronic computers) ; Compilers (Computer programs) ; Python (Computer program language) ; C (Computer program language) ; Electronic videos ; local
    Abstract: "In this Learning Cython training course, expert author Caleb Hattingh will teach you how to create your own simple extension modules in Cython, analyze performance of Cython code, and package your Cython extension module so it can be shared with others. This course is designed for users that already have a basic working knowledge of Python. You will start by learning the basics of Cython, such as how to compile Cython, add tests, and add types. From there, Caleb will teach you how to explore Cython with the Jupyter Notebook. This video tutorial will also teach you about the Cython language, such as adding types to code, exceptions and docstrings, and loops and memoryviews. You will also cover extension types, wrapping C and C++ libraries, Cython compiler directives, and concurrency. Finally, Caleb will leave you with some tips and tricks."--Resource description page.
    Note: Title from title screen (Safari, viewed March 29, 2016). - Date of release from resource description page (Safari, viewed March 29, 2016)
    URL: lizenzpflichtig
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  • 58
    Online Resource
    Online Resource
    Matplotlib for developers : data visualization and analysis with Python (2016)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771375429
    Language: English
    Pages: 1 online resource (1 streaming video file (5 hr., 1 min., 12 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Information visualization ; Electronic videos ; local
    Abstract: "In this Matplotlib for Developers training course, expert author Christopher Roach will teach you everything you need to know about data visualization and analysis with Python. This course is designed for users that already have a basic understanding of Python. You will start by learning about pyplot, including learning about the plot function, custom plotting, subplots, and complex subplots. From there, Christopher will teach you about the matplotlib architecture, how to configure matplotlib, and about interactivity. Finally, this video tutorial covers maps in matplotlib and the matplotlib ecosystem. Once you have completed this computer based training course, you will have learned everything you need to know about data visualization and analysis with Python. Working files are included, allowing you to follow along with the author throughout the lessons."--Resource description page.
    Note: Title from title screen (viewed April 1, 2016). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 59
    Online Resource
    Online Resource
    Working with Unicode in Python : how to handle expanded character sets and multilingual data (2016)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771376105
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 48 min., 20 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Unicode (Computer character set) ; Computer programming ; Electronic videos ; local
    Abstract: "In this Working with Unicode in Python training course, expert author Alexander Lee will teach you how to handle expanded character sets and multilingual data in Python. This course is designed for users that already have a basic working knowledge of Python. You will start by learning about text encodings. From there, Alexander will teach you about Python strings, including how to convert between text and bytes and how to use Unicode in source files. This video tutorial also teaches you how to read and write Unicode data. Finally, you will learn about special Unicode functions, such as normalization, case folding, and sorting. Once you have completed this computer based training course, you will have learned how to handle expanded character sets and multilingual data in Python."--Resource description page.
    Note: Title from title screen (viewed March 24, 2016). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 60
    Online Resource
    Online Resource
    Advanced machine learning with scikit-learn : tools and techniques for predictive analytics in Python (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771374927
    Language: English
    Pages: 1 online resource (1 streaming video file (3 hr., 44 min., 6 sec.)) , digital, sound, color
    Keywords: Machine learning ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "In this Advanced Machine Learning with scikit-learn training course, expert author Andreas Mueller will teach you how to choose and evaluate machine learning models. This course is designed for users that already have experience with Python. You will start by learning about model complexity, overfitting and underfitting. From there, Andreas will teach you about pipelines, advanced metrics and imbalanced classes, and model selection for unsupervised learning. This video tutorial also covers dealing with categorical variables, dictionaries, and incomplete data, and how to handle text data. Finally, you will learn about out of core learning, including the sci-learn interface for out of core learning and kernel approximations for large-scale non-linear classification. Once you have completed this computer based training course, you will have learned everything you need to know to be able to choose and evaluate machine learning models. Working files are included, allowing you to follow along with the author throughout the lessons. "--Resource description page.
    Note: Title from title screen (viewed October 26, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 61
    Online Resource
    Online Resource
    Building web apps using Flask and Neo4j : a guide for Python developers (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771374859
    Language: English
    Pages: 1 online resource (1 streaming video file (2 hr., 39 min., 14 sec.)) , digital, sound, color
    Keywords: Web applications ; Development ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "In this Building Web Apps Using Flask and Neo4j training course, expert author Nicole White will teach you how to incorporate graph databases into your web applications. This course is designed for the absolute beginner, meaning no previous experience with Flask or Neo4j is required. You will start by learning how to install Neo4j and set up your project. From there, Nicole will teach you about Flask, Py2neo, and basic functionality, including constraints and indexes, registering a user, and adding a post. This video tutorial also covers social recommendations, including recommending similar users and commonalities between two users. Finally, you will learn about scaling considerations and how to deploy to Heroku. Once you have completed this computer based training course, you will have learned how to incorporate Neo4j into your own web applications. Working files are included, allowing you to follow along with the author throughout the lessons."--Resource description page.
    Note: Title from title screen (viewed October 21, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 62
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    Online Resource
    Functional programming with Python : techniques for maintainable, modular, and testable Python code (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771374651
    Language: English
    Pages: 1 online resource (1 streaming video file (2 hr., 55 min., 5 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Python (Computer program language) ; Testing ; Functional programming (Computer science) ; Electronic videos ; local
    Abstract: "In this Functional Programming with Python training course, expert author Chris Armstrong will teach you functional programming techniques for making maintainable, modular, and testable code. This course is designed for users that already have experience with Python. You will start by learning about functional programming, including first class functions, recursion, and modifying data structures. From there, Chris will teach you about many functional programming libraries available in the Python ecosystem. This video tutorial will then teach you how to implement a text adventure game using these techniques and libraries. You will also learn how to write unit tests for functional code, use imperative libraries in a functional way, and use first-class effects. Finally, you will create a web UI for the game, and learn how to isolate an imperative web framework so that all of the application logic remains purely functional. Once you have completed this computer based training course, you will have learned how to apply these functional programming techniques to create maintainable, modular, and testable Python code."--Resource description page.
    Note: Title from title screen (viewed December 22, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 63
    Online Resource
    Online Resource
    Porting from Python 2 to Python 3 : tips and techniques for a smooth transition (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771374514
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 31 min., 20 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Object-oriented programming (Computer science) ; Electronic videos ; local
    Abstract: "In this Porting from Python 2 to Python 3 training course, expert author Ben Straub teaches you about the changes with Python 3, and how to make a smooth transition from Python 2 to Python 3. This course is designed for users that already have Python programming experience. You will start by learning about numbers and strings. From there, Ben teaches you about exceptions, such as raising and catching, and tracebacks and chaining. This video tutorial also covers classes, IO, and library. Finally, you will learn other language changes, such as print, imports and reload, ranges, and functional operators. Once you have completed this computer based training course, you will have learned all about Python 3 changes, and how to make a smooth transition from Python 2 to Python 3."--Resource description page.
    Note: Title from title screen (viewed September 28, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 64
    Online Resource
    Online Resource
    Teach your kids to code : basic concepts with Turtle graphics in Python (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781491951071
    Language: English
    Pages: 1 online resource (1 streaming video file (45 min., 10 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Electronic videos ; local
    Abstract: "Turtle graphics have been teaching kids basic coding concepts since the creation of the Logo language in the late 1960's. The Turtle module in Python carries the turtle tradition into the 21st century and makes turtle programming easy and accessible on Windows, Mac, Linux and more. Turtle graphics in Python are a great way to introduce kids to coding, staring with short programs of just five to ten lines of code that draw beautiful, colorful shapes that kids can create and modify as they learn. In his new book, Teach Your Kids to Code, Bryson Payne shares some of the programs he began using with his own sons when they were two and four years old, all the way through fully interactive games he uses in his freshman-level college courses.In this webcast, you will learn: How to code turtle graphics in Python; How to engage children from pre-K through college with short, interactive, visual programs for every age level; How to build on more advanced programming concepts using turtle graphics as a foundation."--Resource description page.
    Note: Title from title screen (viewed February 12, 2016)
    URL: lizenzpflichtig
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  • 65
    Online Resource
    Online Resource
    Python game development for beginners : create interactive user interfaces and games with the turtle module (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771374071
    Language: English
    Pages: 1 online resource (1 streaming video file (2 hr., 34 min., 59 sec.)) , digital, sound, color
    Keywords: Computer games ; Programming ; Computer games ; Design ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "In this Python Game Development for Beginners training course, expert author Elliot Hauser will teach you how to build your own game sketches using Python's built-in turtle module. This course is designed for the absolute beginner, meaning no Python or game development experience is required. You will start by learning about simple user interfaces, then jump into learning how to make your own game objects with class inheritance. From there, Elliot will teach you about the game state dictionary, how to check for intersection, and how to display game state. Finally, this video tutorial covers some advanced topics, including adding images to Turtle and private methods and attributes. Once you have completed this computer based training course, you will be fully capable of creating your own user interfaces and games with the turtle module. Working files are included, allowing you to follow along with the author throughout the lessons."--Resource description page.
    Note: Title from title screen (viewed September 4, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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    Online Resource
    Online Resource
    Web applications with Python and the pyramid framework : from views and routes to forms and authentication (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781771374361
    Language: English
    Pages: 1 online resource (1 streaming video file (4 hr., 29 min., 53 sec.)) , digital, sound, color
    Keywords: Web applications ; Development ; Python (Computer program language) ; Electronic videos ; local
    Abstract: "In this Web Applications with Python and the Pyramid Framework training course, expert author Paul Everitt will teach you about the features needed for Python web development, as well as Pyramid's unique features. This course is designed for users that already have a basic knowledge of Python. You will start by learning about single file web apps, templating, and multiple routes and views. From there, Paul will teach you about MyApp Python package, views and routes, and templating and static assets. This video tutorial also covers forms, databases, and sessions, authentication and authorization, and JSON. Finally, you will learn about extensibility, including custom configuration settings, extending and overriding, and custom view predicates. Once you have completed this computer based training course, you will have gained a basic understanding of the features needed for Python web development and the features unique to Pyramid."--Resource description page.
    Note: Title from title screen (viewed December 22, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 67
    Online Resource
    Online Resource
    Building scalable web apps with Python and Google Cloud platform (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781491939765
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 27 min., 15 sec.)) , digital, sound, color
    Keywords: Web applications ; Development ; Python (Computer program language) ; Cloud computing ; Electronic videos ; local
    Abstract: "We all know Python is well suited to building web applications. It also happens to be a great choice for building on Google Cloud Platform, Google's suite of cloud hosting services. In this presentation, Google engineer and O'Reilly author Dan Sanderson shows how to set up a Python cloud development environment, build a scalable web app using various Google services and tools, and deploy and manage a live application."--Resource description page.
    Note: Title from title screen (viewed November 4, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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  • 68
    Online Resource
    Online Resource
    Designing data structures in Python : principles & practice (2015)
    [Place of publication not identified] : O'Reilly
    Details
    ISBN: 9781491928622
    Language: English
    Pages: 1 online resource (1 streaming video file (6 hr., 4 min., 21 sec.)) , digital, sound, color
    Keywords: Python (Computer program language) ; Data structures (Computer science) ; Computer programming ; Electronic videos ; local
    Abstract: "When should you use Python's built-in data types, and when should you develop your own? In this video course, George Heineman introduces Python programmers to several important data structures and demonstrates their use with example algorithms. Generic data structures such as arrays, linked lists, and stacks can solve many problems, but to work through some specialized problems, you need to learn different ways to structure data appropriately. Many Python programmers learned their skills through non-traditional routes, rather than through an undergraduate computer science degree. This video helps complete your education in fundamental data types step-by-step. For many of the data structures, you'll write sample code using a variety of existing modules, and define a process that will help you evaluate and assess these modules for use in your own software. All you need to get started is a working knowledge of Python's built-in data types."--Resource description page.
    Note: Title from title screen (viewed October 14, 2015). - Date of publication from resource description page
    URL: lizenzpflichtig
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