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  • MPI Ethno. Forsch.  (25)
  • English  (25)
  • Packt Publishing,  (25)
  • International Energy Agency
  • Machine learning  (25)
  • 1
    Online Resource
    Online Resource
    Machine learning : modern computer vision & generative AI (2024)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781835883907 , 1835883907
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 4 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Artificial intelligence ; Apprentissage automatique ; Intelligence artificielle ; artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: This is an innovative course tailored for those who aspire to master the dynamic fields of computer vision and generative artificial intelligence. This course offers a deep dive into the world of image recognition, object detection, and the magic of generative AI using tools like KerasCV, Python, TensorFlow, PyTorch, and JAX. This course guides you from the fundamentals of image classification to the intricacies of object detection, starting with the KerasCV library for deep learning. You'll learn to effectively use and fine-tune pre-trained models, create custom datasets for object detection, using tools like the LabelImg GUI program, and apply these skills to tackle real-world challenges. A key focus is the generative AI segment, particularly on Stable Diffusion, where you'll learn to generate detailed images from text, unlocking your creative potential in this advanced AI domain. This course offers a progressive learning path from basic concepts to advanced techniques, catering to both professional development and creative pursuits in AI. By the end, you'll have the confidence and tools needed for a variety of applications in the ever-evolving field of generative artificial intelligence. What you will learn Harness the power of the KerasCV library for efficient deep learning Master image classification techniques using pre-trained models Implement object detection in real-world scenarios Fine-tune models for tailored applications and datasets Create custom object detection datasets with LabelImg Understand the integration of TensorFlow, PyTorch, and JAX in computer vision Audience This course is ideal for individuals ranging from beginners to advanced learners who possess a fundamental understanding of machine learning and Python programming. It is especially beneficial for those keen on delving into the realm of computer vision using KerasCV. About the Author Lazy Programmer: The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.
    Note: Online resource; title from title details screen (O'Reilly, viewed January 30, 2024)
    URL: lizenzpflichtig
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  • 2
    Online Resource
    Online Resource
    Math 0-1 : matrix calculus in data science and machine learning (2024)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781835886649 , 1835886647
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 17 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Computer science Mathematics ; Apprentissage automatique ; Informatique ; Mathématiques ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: This course starts with an introduction to the key concepts and outlines the roadmap to success in the field. You'll begin by understanding the foundational elements of matrix and vector derivatives, exploring topics like linear and quadratic forms, chain rules in matrix form, and the derivative of determinants. Each concept is reinforced with exercises, ranging from quadratic challenges to least squares and Gaussian methods. The course progresses into optimization techniques essential in data science and machine learning. Delve into multi-dimensional second derivative tests, gradient descent in one and multiple dimensions, and Newton's method, including practical exercises in Newton's Method for least squares. An additional focus is set on setting up your environment, where you'll learn to establish an Anaconda environment and install crucial tools like Numpy, Scipy, and TensorFlow. The course also addresses effective learning strategies, answering pivotal questions like the suitability of YouTube for learning calculus and the recommended order for taking courses in this field. As you journey through the course, you'll transition from foundational concepts to advanced applications, equipping yourself with the skills needed to excel in data science and machine learning. What you will learn Understand matrix and vector derivatives Master linear and quadratic forms Apply the chain rule in matrix calculus Solve optimization problems using gradient descent and Newton's method Set up the Anaconda environment for machine learning Install and use key libraries like Numpy and TensorFlow Develop effective strategies for learning calculus in data science Audience This course suits students and professionals eager to learn the math behind AI, Data Science, and Machine Learning, ideal for deepening knowledge in these advanced technology fields. Learners should have a basic knowledge of linear algebra, calculus, and Python programming to effectively understand matrix calculus. A keen interest and enthusiasm for exploring this intricate subject are also crucial for a fulfilling learning experience. About the Author Lazy Programmer: The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.
    Note: Online resource; title from title details screen (O'Reilly, viewed January 30, 2024)
    URL: lizenzpflichtig
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  • 3
    Online Resource
    Online Resource
    Deep learning : Artificial neural networks with Tensorflow (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781804617250 , 1804617253
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 48 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: TensorFlow ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI and machine learning. So, if you want to do deep learning, you got to know TensorFlow. In this course, you will learn how to use TensorFlow 2 to build deep neural networks. We will first start by learning the basics of machine learning, classification, and regression. Then in the next section, we will understand the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning. In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization. By the end of this course, we will have understood how to use TensorFlow for artificial neural networks in deep learning. What You Will Learn Understand what machine learning is Build linear models with TensorFlow 2 Learn how to build deep neural networks with TensorFlow 2 Learn how to perform image classification and regression with ANN Learn loss functions such as mean-squared error and cross-entropy loss Learn about stochastic gradient descent, momentum, and Adam optimization Audience This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers. One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 20, 2023)
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  • 4
    Online Resource
    Online Resource
    Statistics and mathematics for data science and data analytics (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781837632336 , 1837632332
    Language: English
    Pages: 1 online resource (1 video file (11 hr., 24 min.)) , sound, color.
    Edition: [First edition].
    DDC: 519.5
    Keywords: Statistics Data processing ; Mathematics Data processing ; Probabilities ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: If you aim for a career in data science or data analytics, this course will equip you with the practical knowledge needed to master basic statistics. You need good statistics and probability theory knowledge to become a data scientist or analyst. The course begins with an introduction to descriptive statistics and explains the basics, including the mean, median, mode, and skewness. You will then learn more about ranges, interquartile range (IQR), samples and populations, variance, and standard deviation. The following section will explain distributions in detail, including normal distribution and Z-scores. Then, you will explore probability in detail, go over the Bayes theorem, the Central Limit theorem, the law of large numbers, and finally, Poisson's distribution. Next, you will comprehensively explore linear regression and the coefficients of regression, mean square error, mean absolute error, and root mean square error. You will also explore hypothesis testing and type I and II errors in more detail and then learn comprehensively about the analysis of variance (ANOVA). After completing this course, you will comprehensively acquire knowledge about statistical fundamentals, data analysis methods, decision-making processes, and machine learning concepts with examples. What You Will Learn Master basic statistics, descriptive statistics, and probability theory Explore ML methods, including decision trees and decision forests Learn probability distributions normal and Poisson distributions Explore hypothesis testing, p-values, types I and II error handling Master logistic regression, linear regression, and regression trees Learn correlation, R-Square, RMSE, MAE, and coefficient of determination Audience This beginner-level course has been niched to cater to an individual looking to master statistics and probability for data science and analysis, an individual looking to pursue a career in data science, or professionals and students wanting to understand statistics for data analysis. The prerequisites for this course include absolutely no previous experience required and an eagerness and motivation to learn. About The Author Nikolai Schuler: Nikolai Schuler, as a data scientist and BI consultant, believes that the data world benefits from new tools and technologies, but it is extremely difficult to get trained in the field as practical courses with quality content are rare or are structured incompatible with a busy working life. Nikolai's courses offer precious content and have an easy-to-follow structure. He aims to help anyone wishing to pursue their desired career by upgrading their data analysis skills. His courses have already found their audience in over 170 countries with numerous positive feedback and will equip you with the skillsets to master data science and analytics! If you are looking for qualitatively approachable training, then jump on board!.
    Note: "Published in January 2023.". - Online resource; title from title details screen (O'Reilly, viewed February 20, 2023)
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  • 5
    Online Resource
    Online Resource
    Advanced chatbots with deep learning and Python (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781837632534 , 1837632537
    Language: English
    Pages: 1 online resource (1 video file (1 hr., 59 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.33
    Keywords: Human-computer interaction ; Machine learning ; Python (Computer program language) ; Artificial intelligence
    Abstract: AI-powered chatbots are also capable of automating various tasks, including sales and marketing, customer service, and administrative and operational tasks. In this course about developing advanced chatbots with deep learning, we will understand their applications and build from scratch using deep learning with Python The course begins with a brief overview and the fundamentals of deep learning for chatbots. We will understand and compare conventional chatbots with deep learning-based chatbots. Then, we will explore self-learning chatbots, including generative chatbots and retrieval chatbots. You will learn more about deep learning-empowered chatbot features and compare and distinguish the abilities of conventional chatbots and self-learning chatbots in real action. We will focus on chatbot development with deep learning, tokenization, setting up an Encoder-Decoder, implementing RNN-based model development, and finally, training, testing, and validating the chatbot we developed. Upon completing this course successfully, you will relate concepts and understand theories of chatbots in various domains, understand and implement deep learning models for building real-world chatbots, and evaluate deep learning-based chatbot models. What You Will Learn Relate the concepts and theories for chatbots in various domains Compare conventional chatbots with deep learning-based chatbots Understand deep learning algorithms for chatbots Implement deep learning models for building real-world chatbots Learn about tokenization and setting up an encoder-decoder Implement recurrent neural network-based model development Audience This course is designed for individuals looking to advance their skills in applied deep learning, acquire knowledge regarding the relationships of data analysis with deep learning, wish to build customized chatbots for their applications, learn to implement deep learning algorithms for chatbots, and are passionate about rule-based and self-learning chatbots. Deep learning practitioners/scholars working on chatbot concepts would benefit from this course. No prior knowledge of chatbots, deep learning, data analysis, or mathematics is needed. Basic to intermediate Python knowledge is required. About The Author AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
    Note: "Published in February 2023."
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  • 6
    Online Resource
    Online Resource
    Deep learning : Convolutional neural networks with TensorFlow (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781801076272 , 1801076278
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 41 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: TensorFlow ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI (Artificial Intelligence) and machine learning. So, if you want to do deep learning, you must know TensorFlow. In this course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNN). We will first start by having an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. Then you will learn how to apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. Next, you will learn how to perform text preprocessing and text classification with CNNs In the last section, you will learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning for Computer Vision. By the end of this course, we will have understood how to build convolutional neural networks in deep learning with TensorFlow. What you Will Learn Understand the concept of convolution Integrate convolution into neural networks Apply CNNs to several image recognition datasets, both small and large Learn best practices for designing CNN architectures Learn about batch normalization and data augmentation Learn how to preform text preprocessing Audience This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement convolutional neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 20, 2023)
    URL: lizenzpflichtig
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  • 7
    Online Resource
    Online Resource
    Deep learning : computer vision for beginners using PyTorch (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781837634286 , 1837634289
    Language: English
    Pages: 1 online resource (1 video file (7 hr., 18 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Deep learning (Machine learning) ; Machine learning ; PyTorch (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Note: The course is primarily focused on teaching PyTorch and deep learning for computer vision, but it also includes a few sections on the fundamentals of Python (Sections 8-12). These optional learning sections are designed for individuals who may be new to Python or who want to refresh their knowledge of Python basics. In this course, we will take a step-by-step method by first grasping PyTorch's fundamentals. Then, using a guide to getting free GPU for learning, you will learn how to code in GPU. You will then learn about PyTorch's AutoGrad feature and how to use it. Later, you will learn how to use PyTorch to create deep learning models and understand the fundamentals of convolutional neural networks (CNN). You will also learn how to use CNN with a real-world dataset. Additionally, the course will emphasize the fundamentals and lay the groundwork for an understanding of Python. We will also talk about the three significant Python libraries known as NumPy, Pandas, and Matplotlib. In this part of the course, we will also build a mini project where we will be building a hangman game in Python. By the end of this course, we will be able to perform Computer Vision tasks with deep learning. What You Will Learn Learn how to work with PyTorch Build intuition on convolution operation on images Implement gradient descent using AutoGrad Learn about LeNet architecture Create a mini-Python project game Understand how to use NumPy, Pandas, and Matplotlib libraries Audience Software developers, machine learning practitioners, data scientists, and anybody else interested in understanding PyTorch and deep learning should take this course. While a basic knowledge of Python would be beneficial, it is not a prerequisite as we will be covering the necessary fundamentals during the course. About The Author Manifold AI Learning: Manifold AI Learning℗ is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence. With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 11, 2023)
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  • 8
    Online Resource
    Online Resource
    Recommender systems with machine learning (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781837631667 , 1837631662
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 17 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.5/6
    Keywords: Recommender systems (Information filtering) ; Machine learning ; Artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Have you ever thought how YouTube adjusts your feed as per your favorite content? Ever wondered! Why is your Netflix recommending your favorite TV shows? Have you ever wanted to build a customized recommender system for yourself? Then this is the course you are looking for. We will begin with the theoretical concepts and fundamental knowledge of recommender systems. You will gain an understanding of the essential taxonomies that form the foundation of these systems. You will be learning how to use the power of Python to evaluate your recommender systems datasets based on user ratings, user choices, music genres, categories of movies, and their year of release. A practical approach will be adopted to build content-based filtering and collaborative filtering techniques for recommender systems. Moving ahead, you will learn all the basic and necessary concepts for the applied recommender systems models along with the machine learning models. Moreover, various projects have been included in this course to develop a very useful experience for you. By the end of this course, you will be able to relate the concepts and theories for recommender systems in various domains, implement machine learning models for building real-world recommendation systems, and evaluate the machine learning models. What Yoy Will Learn Explore AI-integrated recommender systems basics Look at the basic taxonomy of recommender systems Study the impact of overfitting, underfitting, bias, and variance Build content-based recommender systems with ML and Python Build item-based recommender systems using ML techniques and Python Learn to model KNN-based recommender engine for applications Audience No prior knowledge of recommender systems, machine learning, data analysis, or mathematics is needed. Only the working knowledge of basics of Python is required. You will start from the basics and gradually build your knowledge in the subject. This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs. The course is suitable for individuals who want to advance their skills in ML, master the relation of data analysis with ML, build customized recommender systems for their applications, and implement ML algorithms for recommender systems. About The Author AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
    Note: "Published March 2023.". - Online resource; title from title details screen (O'Reilly, viewed April 11, 2023)
    URL: lizenzpflichtig
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  • 9
    Online Resource
    Online Resource
    PyTorch ultimate 2023 : from basics to cutting-edge (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781801070089 , 1801070083
    Language: English
    Pages: 1 online resource (1 video file (17 hr., 39 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/2
    Keywords: Neural networks (Computer science) ; Machine learning ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: PyTorch is a Python framework developed by Facebook to develop and deploy deep learning models. It is one of the most popular deep-learning frameworks nowadays. You will begin with learning the deep learning concept. Dive deeper into tensor handling, acquiring the finesse to create and manipulate tensors while leveraging PyTorch's automatic gradient calculation through Autograd. Then transition to modeling by constructing linear regression models from scratch. After that, you will dive deep into classification models, mastering both multilabel and multiclass. You will then see the theory behind object detection and acquire the prowess to build object detection models. Embrace the cutting edge with YOLO v7, YOLO v8, and faster RCNN, and unleash the potential of pre-trained models and transfer learning. Delve into RNNs and look at recommender systems, unlocking matrix factorization techniques to provide personalized recommendations. Refine your skills in model debugging and deployment, where you will debug models using hooks, and navigate the strategies for both on-premise and cloud deployment. Finally, you will explore ChatGPT, ResNet, and Extreme Learning Machines. By the end of this course, you will have learned the key concepts, models, and techniques, and have the confidence to craft and deploy robust deep-learning solutions. What You Will Learn Grasp deep learning concepts and install tools/packages/IDE/libraries Master CNN theory, image classification, layer dimensions, and transformations Dive into audio classification using torchaudio and spectrograms Do object detection with the help of YOLO v7, YOLO v8, and Faster RCNN Learn word embeddings, sentiment analysis, and pre-trained NLP models Deploy models using Google Cloud and other strategies Audience This course is ideal for Python developers and data enthusiasts seeking to expand their skills. This will also benefit aspiring data scientists, machine learning engineers, AI enthusiasts, and anyone intrigued by the transformative potential of deep learning. Whether you are a beginner or possess some prior knowledge, this course offers a smooth progression that will empower you to develop, deploy, and innovate with deep learning models using PyTorch. Basic Python knowledge is required to fully engage with the material. About The Author Bert Gollnick: Bert Gollnick is a proficient data scientist with substantial domain knowledge in renewable energies, particularly wind energy. With a rich background in aeronautics and economics, Bert brings a unique perspective to the field. Currently, Bert holds a significant role at a leading wind turbine manufacturer, leveraging his expertise to contribute to innovative solutions. For several years, Bert has been a dedicated instructor, offering comprehensive training in data science and machine learning using R and Python. The core interests of Bert lie at the crossroads of machine learning and data science, reflecting a commitment to advancing these disciplines.
    Note: "Updated September 2023.". - Online resource; title from title details screen (O'Reilly, viewed October 10, 2023)
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  • 10
    Online Resource
    Online Resource
    Python : complete Python, Django, data science and ML guide (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781835465141 , 1835465145
    Language: English
    Pages: 1 online resource (1 video file (50 hr., 34 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Computer programming ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: "Join us on an immersive Python programming journey, spanning over 50 hours of learning. Whether you're a novice or experienced, this course equips you with vital Python skills for careers and projects. Starting from the basics, grasp Python's core principles and proficiency in real-world functions. As Python's popularity grows, this course readies you for the rising demand for Python developers. You'll practice hand-on examples using Python's interpreter and Visual Studio Code with Code Runner to solidify your skills. With a focus on Data Science and Machine Learning, you'll master essential packages such as NumPy, Pandas, Matplotlib, and Scikit-learn, using the versatile Jupyter Notebook. The course extensively covers Python's fundamental aspects, spanning variables, lists, dictionaries, and venturing into advanced topics like classes, loops, modules, and creating virtual environments. The goal is to provide you with a solid Python foundation. You'll also gain insight into functional and object-oriented Python programming, making you a versatile coder. The course is thoughtfully structured, explaining not just ""how"" but also ""why"" we use specific methods and best practices. By course end, you'll harness Python's full potential for web and mobile app development, data science, machine learning, and game creation. What You Will Learn Grasp concepts such as data types, loops, and conditional statements to build a robust coding foundation Understand OOP principles like inheritance, encapsulation, and polymorphism for streamlined code Manipulate files, directories, and efficiently manage external modules through Python Master real-world datasets with NumPy, Pandas, Matplotlib and more Ensure code reliability through Python's error handling and master the nuances of PIP and virtual project isolation Audience This comprehensive Python course is tailored for a diverse audience. It's an excellent choice for beginners taking their first steps in programming. If you're interested in data science and machine learning, this course equips you with essential skills. Web developers can leverage Python for building web applications. Moreover, if you're keen on tasks involving machine learning and data processing, this course is for you. Game developers looking to create games using Python and Pygame will find this course invaluable. About The Author Bogdan Stashchuk: Bogdan Stashchuk has over 20 years of experience as a software engineering instructor. He excels at breaking down complex topics into easy-to-follow steps. His courses are designed with hands-on exercises, ensuring that learners can actively participate and apply what they learn. From start to finish, students can follow along and complete tasks just as Bogdan demonstrates in his lectures. He also includes challenging assignments with detailed solutions. This approach helps learners understand and remember the material long after they've completed the course. Through his dedication and expertise, Bogdan ensures a valuable and effective learning experience for everyone.".
    Note: "Updated October 2023.". - Online resource; title from title details screen (O'Reilly, viewed November 15, 2023)
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  • 11
    Online Resource
    Online Resource
    Data science : Time series forecasting with Facebook Prophet in Python (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781803237466 , 1803237465
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 12 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Facebook (Electronic resource) ; Python (Computer program language) ; Time-series analysis Data processing ; Machine learning ; R (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Prophet enables Python and R developers to build scalable time series forecasts. This course will help you implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. In this course, you will learn how to use Facebook Prophet to do time series analysis and forecasting. You will learn how the Prophet works under the hood (that is, what are its modeling assumptions?) and the Prophet API (that is, how to write the code). This course is a practice-oriented course, demonstrating how to prepare your data for Prophet, fit a model, and use it to forecast, analyze the results, and evaluate the model's predictions. We will apply Prophet to a variety of datasets, including store sales and stock prices. You will learn how to use Prophet to plot the model's in-sample predictions and forecast. Then, learn how to plot the components of the fitted model. You will also learn how to deal with outliers, missing data, and non-daily (for example, monthly) data. By the end of this course, you will be able to use Prophet confidently to forecast your data. What You Will Learn Prepare your data (a Pandas dataframe) for Facebook Prophet Learn how to fit a Prophet model to a time series Plot the components of the fitted model Model holidays and exogenous regressors Evaluate your model with forecasting metrics Learn how to do changepoint detection with Prophet Audience Anyone interested in data science, machine learning, or who wishes to use time series analysis on their own data should take this course. Good Python programming skills are required, as well as knowledge of Pandas, Dataframes, and preferably some familiarity with Scikit-Learn, though this is not required. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 20, 2023)
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  • 12
    Online Resource
    Online Resource
    Data science prerequisites : Numpy, Matplotlib, and Pandas in Python (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781803241616 , 1803241616
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 23 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Deep learning (Machine learning) ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science. In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression. In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems. Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib. Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function. Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing. Finally, we will also cover some basics of machine learning that will help us start our deep learning journey. By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science. What You Will Learn Understand supervised machine learning with real-world examples Understand and code using the NumPy stack Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms Understand the pros and cons of various machine learning models Get a brief introduction to the classification and regression Learn how to calculate the PDF and CDF under the normal distribution Audience This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms. One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 11, 2023)
    URL: lizenzpflichtig
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  • 13
    Online Resource
    Online Resource
    Recommender systems : an applied approach using deep learning (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781837638062 , 1837638063
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 2 min.)) , sound, color.
    Edition: [First edition].
    DDC: 025.04
    Keywords: Recommender systems (Information filtering) ; Artificial intelligence ; Machine learning ; Artificial intelligence ; Machine learning ; Recommender systems (Information filtering) ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for products recommendation, prepare data, and model development using a two-tower approach. You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system. Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems. What You Will Learn Learn about deep learning and recommender systems Explore the mechanisms of deep learning-based approaches Learn to implement a two-tower model for recommenders Implement TensorFlow to develop a recommender system Learn basic neural network models for recommendations Explore neural collaborative filtering and variational autoencoders Audience This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. Individuals passionate about recommender systems with the help of TensorFlow Recommenders will benefit from this course. Deep learning practitioners, research scholars, and data scientists will also benefit from the course. The prerequisites include a basic to intermediate knowledge of Python and Pandas library. About The Author AI Sciences: AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.
    Note: "Published in February 2023.". - Online resource; title from title details screen (O'Reilly, viewed March 21, 2023)
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  • 14
    Online Resource
    Online Resource
    Basics of chatbots with machine learning & Python (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781837635092 , 1837635099
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 4 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.33
    Keywords: Human-computer interaction ; Machine learning ; Python (Computer program language) ; Artificial intelligence ; Artificial intelligence ; Human-computer interaction ; Machine learning ; Python (Computer program language) ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Chatbots are software applications used for online chat conversations through text or text-to-speech instead of providing direct contact with a live human agent. Chatbots are used in dialog systems for various purposes, including customer service, request routing, or information gathering. This course begins with a brief overview of chatbots, their need, and the types of chatbots. We will explore rule-based versus self-learning chatbots. We will understand the working mechanism of chatbots. We will explore machine learning-based chatbots and understand the ML-based architecture of chatbots. You will learn about the purpose of ML-based chatbots and their impact. We will get an overview of the Natural Language Toolkit (NLTK). You will learn to install packages and create a corpus with Python. We will delve into text preprocessing and helper function deployment, generate responses, and implement term-frequency times inverse document-frequency. We will train and test rule-based chatbots and finally work on a project developing an artificial intelligence question-answer chatbot using NLTK. Upon course completion, you will be able to relate the concepts and theories for chatbots in various domains, understand and implement machine learning models for building real-time chatbots, and evaluate machine learning models in chatbots. What You Will Learn Learn about chatbot types, rule-based and self-learning chatbots Learn text preprocessing and develop helper functions with Python Explore the impact and overview of the Natural Language Toolkit Gain hands-on practice, generate text in Python to develop chatbots Explore testing and training of chatbot with machine learning Implement term-frequency times inverse document-frequency hands-on Audience This course delivers content to people wishing to advance their skills in applied machine learning, master data analysis with machine learning, build customized chatbots for their applications, and implement machine learning algorithms for chatbots. This course is for you if you are passionate about rule-based and conversational chatbots. Machine learning practitioners, research scholars, and data scientists can benefit from the course. No prior knowledge of chatbots, or machine learning, is needed. You will need to know basic to intermediate Python coding, which is not taught separately in the course. About The Author AI Sciences: AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.
    Note: "Published in February 2023.". - Online resource; title from title details screen (O'Reilly, viewed March 21, 2023)
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  • 15
    Online Resource
    Online Resource
    Fundamentals of machine learning (2023)
    [Birmingham, United Kingdom] : Packt Publishing
    Details
    ISBN: 9781837635719 , 1837635714
    Language: English
    Pages: 1 online resource (1 video file (8 hr., 42 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Machine learning is a branch of AI and computer science that focuses on the use of data to imitate the way humans learn and improve its accuracy. The course is divided into two parts. The first part starts with a brief history of how machine learning started and introduces you to the basics of statistical learning. You will also understand linear regression and classification, which is the logistic regression model. Understand what cross-validation, sampling, and Bootstrap are. Explore how to go beyond linearity; we will specifically look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear. The second part of the course is completely hands-on labs, which start with an example of predicting fuel efficiency in linear regression. We will then look at a lab on logistic regression with a little bit of mathematics behind it. Understand another lab session on random forests and do a review of decision trees as well. Next, we will look at a lab session on Eigenfaces by using Principle Component Analysis (PCA) and wrap up a course with a lab on ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve). By the end of the course, you would have given yourself the skills and confidence to start programming machine learning algorithms. What You Will Learn Learn the basics of statistical learning Understand linear regression, classification, and supervised learning Understand sampling and Bootstrap in machine learning Explore model selection and regularization Understand random forests and decision trees Explore labs on Multilayer Perceptron (MLP) and RNN Audience This course can be taken by beginners in Python programming, machine learning, and data science. Scientists, data scientists, and data analysts can also opt for this course. The course assumes no prior knowledge. However, some prior training in Python programming and some basic calculus knowledge is helpful for the course. About Authors Yiqiao Yin: Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP. Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.
    Note: Online resource; title from title details screen (O’Reilly, viewed February 7, 2023)
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  • 16
    Online Resource
    Online Resource
    Deep learning : Recurrent neural networks with TensorFlow (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781803242828 , 1803242825
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 7 min.)) , sound, color.
    DDC: 006.3/1
    Keywords: TensorFlow ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions. TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs. In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will apply LSTMs to stock "price" predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do and how not to make the same mistakes that most blogs and courses make when predicting stocks. By the end of this course, you will be able to build your own build RNNs with TensorFlow 2. What You Will Learn Learn about simple RNNs (Elman unit) Covers GRU (gated recurrent unit) Learn how to use LSTM (long short-term memory unit) Learn how to preform time series forecasting Learn how to predict stock price and stock return with LSTM Learn how to apply RNNs to NLP Audience This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 20, 2023)
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  • 17
    Online Resource
    Online Resource
    Deep learning : crash course 2023 (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781837632558 , 1837632553
    Language: English
    Pages: 1 online resource (1 video file (9 hr., 14 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Deep learning (Machine learning) ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Unlock the power of deep learning and take your machine learning skills to the next level with our comprehensive course on deep neural networks. This hands-on course will provide you with a solid understanding of the fundamentals of deep learning, including artificial neural networks, activation functions, bias, data, and loss functions. You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas. With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. You will explore common activation functions, such as ReLU and SoftMax, and learn how to apply them in real-world applications. Through a series of practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of the most popular deep learning frameworks in use today. You will learn how to create and train deep neural networks, evaluate their performance, and fine-tune them for optimal results. By the end of the course, you will be well on your way to becoming a deep learning expert in no time. What You Will Learn Learn about the fundamentals of Python and some of its well-known libraries Understand the fundamentals of deep learning and neural networks Build and train your own deep neural network models Learn different activation functions and optimization algorithms Learn techniques for improving model performance and reducing overfitting Apply deep learning to real-world problems in various fields Audience This course is suitable for anyone interested in exploring the field of deep learning and building a solid foundation in artificial neural networks. No prior experience in programming or machine learning is required, making it an ideal starting point for beginners. It is ideal for students, professionals, and anyone who wants to enhance their skills and stay up-to-date with the latest developments in the field of artificial intelligence. Whether you are looking to kickstart your career or simply want to explore the exciting world of deep learning, this course is a great choice. About The Author Manifold AI Learning: Manifold AI Learning is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence. With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.
    Note: "Published in April 2023.". - Online resource; title from title details screen (O'Reilly, viewed April 24, 2023)
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  • 18
    Online Resource
    Online Resource
    R ultimate 2023 : R for data science and machine learning (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781835082539 , 183508253X
    Language: English
    Pages: 1 online resource (1 video file (22 hr., 25 min.)) , sound, color.
    Edition: [First edition].
    DDC: 519.50285/5133
    Keywords: R (Computer program language) ; Computer programming ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: R is a programming language and environment designed for statistical computing, data analysis, and graphical representation. R is widely used by statisticians, data scientists, researchers, and analysts for various tasks related to data manipulation, statistical modeling, and visualization. R is particularly well-suited for tasks involving data analysis, visualization, and statistics, chosen for its flexibility and a wide array of available tools. This course takes us on a transformative journey through R programming, from foundational concepts to cutting-edge techniques. We delve into R's fundamentals, data types, variables, and structures. We will explore R programming with custom functions, control structures, and data manipulation. We will analyze data visualization with leading packages, statistical analysis, hypothesis testing, and regression modeling. With regular expressions, we will understand advanced data manipulation, outlier handling, missing data strategies, and text manipulation. We will learn about ML with regression, classification, and clustering algorithms. We will explore DL, neural networks, image classification, and semantic segmentation. Upon completion, we will create dynamic web apps with Shiny and emerge as skilled R practitioners, ready to tackle challenges and contribute to data-driven decision-making. What You Will Learn Excel in R basics and advanced data science techniques Transform, visualize, and aggregate data with precision Craft compelling visuals using ggplot, Plotly, and leaflet Implement regression, classification, and clustering models Explore neural networks, image classification, and segmentation Develop dynamic web apps using R Shiny for engaging user experiences Audience The course caters to aspiring and established data scientists, analysts, programmers, researchers, and professionals seeking to enhance their skills in data manipulation, statistical analysis, ML, and DL using R programming. It caters to individuals with varying experience levels, from beginners looking to enter the field to experienced practitioners aiming to expand their expertise in data-driven decision-making and advanced analytics. Prerequisites include prior programming experience but this course can accommodate learners with varying levels of data science concepts and R programming familiarity. About The Author Bert Gollnick: Bert Gollnick is a proficient data scientist with substantial domain knowledge in renewable energies, particularly wind energy. With a rich background in aeronautics and economics, Bert brings a unique perspective to the field. Currently, Bert holds a significant role at a leading wind turbine manufacturer, leveraging his expertise to contribute to innovative solutions. For several years, Bert has been a dedicated instructor, offering comprehensive training in data science and machine learning using R and Python. The core interests of Bert lie at the crossroads of machine learning and data science, reflecting a commitment to advancing these disciplines.
    Note: "Updated in September 2023.". - Online resource; title from title details screen (O'Reilly, viewed October 11, 2023)
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  • 19
    Online Resource
    Online Resource
    Python for machine learning : the complete beginner's course (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781804619308 , 1804619302
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 29 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Learn Python programming and Scikit-Learn applied to machine learning regression in this comprehensive guide for beginners About This Video Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence Build artificial neural networks with TensorFlow and Keras Make predictions using linear regression, polynomial regression, and multivariate regression In Detail Machine learning is a branch of computer science in which you can use mathematical input to develop complicated models that fulfil various roles. Python is a popular choice for building machine learning models because of the large number of libraries available. This course will walk you through an astonishing combination of Python and machine learning, teaching you the fundamentals of machine learning so you can construct your own projects. We will begin by studying Python programming and applying Scikit-Learn to machine learning regression in this course. After that, we will look at the theory underpinning simple and multiple linear regression algorithms. Following that, we will look at how to solve linear and logistic regression issues. Later, we will use sklearn to learn both the theory and the actual application of logistic regression. We will also go into the math underpinning decision trees. Finally, you will learn about the various clustering algorithms. By the end of this course, you will be able to use these algorithms in the real world. Audience This course is for anyone interested in pursuing a career in machine learning, as well as Python programmers who want to add machine learning skills to their resume. This course will also benefit technologists who want to learn more about how machine learning works in the real world. This course requires familiarity with the fundamentals of Python, as well as readiness, flexibility, a will to learn, and, most importantly, basic mathematical skills.
    Note: "Published in September 2022.". - Online resource; title from title details screen (O'Reilly, viewed October 4, 2022)
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  • 20
    Online Resource
    Online Resource
    Probability / statistics : the foundations of machine learning (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781803241197 , 1803241195
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 36 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Probabilities Data processing ; Statistics Data processing ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Learn how to use probability/statistics in all areas of computer science, data science, and machine learning About This Video A practical approach towards understanding the core concepts of probability and statistics Focuses on the applications of these important mathematical concepts in data science, machine learning, and other areas Understand why probability is the foundation of all modern machine learning In Detail The objective of this course is to give you a solid foundation needed to excel in all areas of computer science--specifically data science and machine learning. The issue is that most of the probability and statistics courses are too theory-oriented. They get tangled in the math without discussing the importance of applications. Applications are always given secondary importance. In this course, we take a code-oriented approach. We apply all concepts through code. In fact, we skip over all the useless theory that isn't relevant to computer science. Instead, we focus on the concepts that are more useful for data science, machine learning, and other areas of computer science. For instance, many probability courses skip over Bayesian inference. We will get to this immensely important concept rather quickly and give it due attention as it is widely thought of as the future of analysis! This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own! Audience This course is designed for beginner ML and data science developers who need a solid foundation, for developers curious about data science and machine learning, for people looking to find out why probability is the foundation of all modern machine learning, or for developers who want to know how to harness the power of big data.
    Note: "Updated in June 2022.". - Online resource; title from title details screen (O'Reilly, viewed July 6, 2022)
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  • 21
    Online Resource
    Online Resource
    Python machine learning bootcamp (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781804619049 , 1804619043
    Language: English
    Pages: 1 online resource (1 video file (24 hr.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: In this course, we will cover many different types of machine learning aspects. We will start by going through a sample machine learning project from an idea to developing a final working model. You will learn many important techniques around data preparation, cleaning, feature engineering, optimization and learning techniques, and much more. Once we have gone through the whole machine learning project, we will then dive deeper into several different areas of machine learning, to better understand each task, and how each of the models we can use to solve these tasks work, and then also using each model and understanding how we can tune all the parameters we learned about in the theory components. We will dive deeper into classification, regression, ensembles, dimensionality reduction, and unsupervised learning. At the end of this course, you should have a solid foundation of machine learning knowledge. You will be able to build machine learning solutions to different types of problems you will come across and be ready to start applying machine learning on the job or in technical interviews. What You Will Learn Learn how to take an ML idea and flush it out into a fully functioning project Learn the different types of ML approaches and the models within each section Get a theoretical and intuitive understanding of how each model works See the practical application and implementation for each model we cover Learn how to optimize models Learn the common pitfalls and how to overcome them Audience This course is designed for beginner Python programmers and data scientists who want to understand ML (Machine Learning) models in depth and be able to use them in practice. Basic Python knowledge is required and some previous experience with the Pandas and Matplotlib libraries will be helpful. About The Author Maximilian Schallwig: Maximilian Schallwig is a data engineer and a proficient Python programmer. He holds a bachelor's degree in physics and a master's degree in astrophysics. He has been working on data for over five years, first as a data scientist and then as a data engineer. He can talk endlessly about big data pipelines, data infrastructure, and his unwavering devotion to Python. Even after two unsuccessful attempts in high school, he still decided to learn Python at the University. He cautiously stepped into the realm of data, beginning with a simple Google search for "what does a data scientist do". He was determined to pursue a career in data science to become a data engineer by learning about big data tools and infrastructure design to build scalable systems and pipelines. He enjoys sharing his programming skills with the rest of the world.
    Note: "Published in December 2022.". - Online resource; title from title details screen (O'Reilly, viewed January 10, 2023)
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 22
    Online Resource
    Online Resource
    No-code machine learning using Amazon AWS SageMaker Canvas (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781804619896 , 1804619892
    Language: English
    Pages: 1 online resource (1 video file (1 hr., 27 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Amazon Web Services (Firm) ; Machine learning ; Amazon Web Services (Firm) ; Machine learning ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: AWS is the #1 cloud-based tool used industry-wide for machine learning projects. You do not need advanced coding expertise that is generally required in the field of machine learning. Nowadays, machine learning models that usually take many days to build are available very quickly in just a few minutes with the help of SageMaker Canvas. SageMaker Canvas has a small learning curve and you can pick up even advanced concepts very quickly. It is also available as part of AWS Free Tier. This course is created with students from various backgrounds in mind. Therefore, we start with the fundamentals and work our way up to more advanced topics. In addition, we will work on four projects in the course itself, from beginning to end. By providing you with in-depth knowledge and the necessary hands-on experience on this newly introduced cloud-based ML tool, this course will help you become a machine learning expert and enhance your skills. You won't need any advanced coding knowledge to complete projects that are based on real-world industry problems. Guidance is offered beyond the tool--you will not only learn the software but also important machine learning principles. By the end of this course, you will be able to build your own machine learning model and get accurate predictions without writing any code using AWS SageMaker Canvas. What You Will Learn Learn the basics of machine learning Get introduced to AWS SageMaker Use the Banknote Authentication dataset to predict data Use the SMS Spam Collection dataset to predict data Use the Customer Churn Prediction 2020 dataset to predict data Use the Wine Quality dataset to predict data Audience This course is designed for students who want to enter the machine learning domain but don't have coding expertise, or anybody in general who wants to know what machine learning is and how to use it professionally. You won't need any high-configuration computer to learn this tool. All you need is any system with internet connectivity and a basic understanding of machine learning. About The Author Prince Patni: Prince Patni is a software developer who specializes in business intelligence and data science. He has studied engineering, worked in four multinational corporations, and has been all over the world for business. He is currently working as an analyst/developer in a reputable organization. He enjoys both teaching and learning. He is here to offer his knowledge of data analytics, data visualization, business intelligence, data science, and other aspects of software development.
    Note: Online resource; title from title details screen (O'Reilly, viewed November 28, 2022)
    URL: lizenzpflichtig
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  • 23
    Online Resource
    Online Resource
    Real-time stream processing using Apache Spark 3 for Python developers (2022)
    [Birmingham, United Kingdom] : Packt Publishing
    Details
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 36 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Scala (Computer program language) ; Python (Computer program language) ; Electronic data processing ; Machine learning ; Scala (Langage de programmation) ; Python (Langage de programmation) ; Apprentissage automatique ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: Build your own real-time stream processing applications using Apache Spark 3.x and PySpark About This Video Learn real-time stream processing concepts Understand Spark structured streaming APIs and architecture Work with file streams, Kafka source, and integrating Spark with Kafka In Detail Take your first steps towards discovering, learning, and using Apache Spark 3.0. We will be taking a live coding approach in this carefully structured course and explaining all the core concepts needed along the way. In this course, we will understand the real-time stream processing concepts, Spark structured streaming APIs, and architecture. We will work with file streams, Kafka source, and integrating Spark with Kafka. Next, we will learn about state-less and state-full streaming transformations. Then cover windowing aggregates using Spark stream. Next, we will cover watermarking and state cleanup. After that, we will cover streaming joins and aggregation, handling memory problems with streaming joins. Finally, learn to create arbitrary streaming sinks. By the end of this course, you will be able to create real-time stream processing applications using Apache Spark. Audience This course is designed for software engineers and architects who are willing to design and develop big data engineering projects using Apache Spark. It is also designed for programmers and developers who are aspiring to grow and learn data engineering using Apache Spark. For this course, you need to know Spark fundamentals and should be exposed to Spark Dataframe APIs. Also, you should know Kafka fundamentals and have a working knowledge of Apache Kafka. One should also have programming knowledge of Python programming.
    Note: Online resource; title from title details screen (O’Reilly, viewed March 10, 2022)
    URL: lizenzpflichtig
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  • 24
    Online Resource
    Online Resource
    Apache Spark 3 advance skills for cracking job interviews (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 48 min.)) , sound, color.
    Edition: [First edition].
    DDC: 650.14/4
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Employment interviewing ; Computer programming Vocational guidance ; Machine learning ; Entretiens d'embauche ; Programmation (Informatique) ; Orientation professionnelle ; Apprentissage automatique ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: An advanced Apache Spark course to help you prepare and crack Spark job interviews. About This Video Practice common job interview questions and answers Deep dive into Spark 3 architecture and memory management Prepare for Databricks Spark certification in a structured way In Detail Apache Spark is a lightning-fast unified analytics engine for big data and machine learning. Since its release, Apache Spark has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Netflix, Yahoo, and eBay have deployed Spark at a massive scale. It has quickly become the largest open-source community in big data. So, mastering Apache Spark opens a wide range of professional opportunities. This course covers some advanced topics and concepts such as Spark 3 architecture and memory management, AQE, DPP, broadcast, accumulators, and multithreading in Spark 3 along with common job interview questions and answers. The objective of this course is to prepare you for advanced certification topics. By the end of this course, you will have learned some advanced topics and concepts that are asked for in the Databricks Spark Certification or Spark job interviews. This will not only help you develop advanced skills in Apache Spark but also crack your job interviews. Audience This course is for anyone who wants to learn and develop advanced skills in Apache Spark or those who are preparing for job interviews and want to learn advanced skills. Before proceeding with the course, you will need basic knowledge of Spark programming in Python - PySpark.
    Note: "Updated in February 2022.". - Online resource; title from title details screen (O'Reilly, viewed March 10, 2022)
    URL: lizenzpflichtig
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  • 25
    Online Resource
    Online Resource
    Real-time stream processing using Apache Spark 3 for scala developers (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 16 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.133
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Scala (Computer program language) ; Electronic data processing ; Machine learning ; Scala (Langage de programmation) ; Apprentissage automatique ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: A comprehensive course for Scala developers to create real-time stream processing applications with Apache Spark. About This Video Deep dive into Spark structured streaming APIs and architecture Discover streaming joins and aggregation Explore real-time stream processing concepts In Detail Since its inception, Apache Spark has seen rapid adoption by enterprises across a wide range of industries. So, mastering Apache Spark opens a wide range of professional opportunities. If you are a software engineer or architect and want to design or build your own projects, then this is the right course for you. This is a hands-on, example-driven, advanced course with demonstrations and coding sessions. This course will help you understand real-time stream processing using Apache Spark and later, you will be able to apply that knowledge to build real-time stream processing solutions. This course covers everything from scratch, which involves installing Apache Spark and seeing how to set up and run Apache Kafka. Furthermore, it introduces stream processing and how to work with files and directories. You will also explore Kafka serialization and deserialization for Spark and how to work with Kafka AVRO Source. And finally, the course wraps up with streaming Watermark and outer joints. By the end of this course, you will be able to design and develop big data engineering projects. You will be able to create real-time stream processing applications with Apache Spark. This course will also help you further your growth in real-time stream processing. Audience This course is designed for software engineers and architects who aspire to develop big data engineering projects using Apache Spark. Also, if you are a programmer and developer who wants to grow and learn data engineering using Apache Spark, then this course is for you. Another group of people that can opt for this course are the managers and architects who might not directly work with Spark implementation but still work with the people who implement Apache Spark at the ground level.
    Note: "Updated in February 2022.". - "ScholarNest.". - Online resource; title from title details screen (O'Reilly, viewed March 10, 2022)
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