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  • 2015-2019  (16)
  • 2016  (16)
  • Birmingham, UK : Packt Publishing  (16)
  • Dordrecht : Springer
  • Machine learning  (16)
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  • 2015-2019  (16)
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  • 1
    Online-Ressource
    Online-Ressource
    Apache Spark machine learning blueprints : develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785887789 , 1785887785
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations.
    Serie: Community experience distilled
    Schlagwort(e): Spark (Electronic resource : Apache Software Foundation) ; Machine learning ; Big data ; Information retrieval ; Electronic books ; Electronic books ; local
    Kurzfassung: Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide About This Book Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development Develop a set of practical Machine Learning applications that can be implemented in real-life projects A comprehensive, project-based guide to improve and refine your predictive models for practical implementation Who This Book Is For If you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required. What You Will Learn Set up Apache Spark for machine learning and discover its impressive processing power Combine Spark and R to unlock detailed business insights essential for decision making Build machine learning systems with Spark that can detect fraud and analyze financial risks Build predictive models focusing on customer scoring and service ranking Build a recommendation systems using SPSS on Apache Spark Tackle parallel computing and find out how it can support your machine learning projects Turn open data and communication data into actionable insights by making use of various forms of machine learning In Detail There's a reason why Apache Spark has become one of the most popular tools in Machine Learning ? its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers. Style and approach This book offers a step-by-step approach to setting up Apache Spark, and use other analytical tools with it to process Big Data and build machine learning pr...
    Anmerkung: Includes index. - Description based on online resource; title from cover (Safari, viewed June 22, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785880391/?ar
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  • 2
    Online-Ressource
    Online-Ressource
    Advanced machine learning with Python : solve challenging data science problems by mastering cutting-edge machine learning techniques in Python (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784393830 , 1784393835
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Serie: Community experience distilled
    Schlagwort(e): Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of m...
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from cover (viewed August 29, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781784398637/?ar
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  • 3
    Online-Ressource
    Online-Ressource
    Large scale machine learning with Python : learn to build powerful machine learning models quickly and deploy large-scale predictive applications (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785888021 , 1785888021
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations.
    Serie: Community experience distilled
    Schlagwort(e): Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a di...
    Anmerkung: Description based on online resource; title from cover (Safari, viewed August 26, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785887215/?ar
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  • 4
    Online-Ressource
    Online-Ressource
    R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784392635 , 1784392634
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations.
    Serie: Community experience distilled
    Schlagwort(e): Machine learning ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully About This Book Get to grips with the concepts of machine learning through exciting real-world examples Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning Learn to build your own machine learning system with this example-based practical guide Who This Book Is For If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary. What You Will Learn Utilize the power of R to handle data extraction, manipulation, and exploration techniques Use R to visualize data spread across multiple dimensions and extract useful features Explore the underlying mathematical and logical concepts that drive machine learning algorithms Dive deep into the world of analytics to predict situations correctly Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Write reusable code and build complete machine learning systems from the ground up Solve interesting real-world problems using machine learning and R as the journey unfolds Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science In Detail Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of ma...
    Anmerkung: Includes index. - Description based on online resource; title from cover page (Safari, viewed April 15, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781784390846/?ar
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  • 5
    Online-Ressource
    Online-Ressource
    Python : deeper insights into machine learning : leverage benefits of machine learning techniques using Python : a course in three modules (2016)
    Birmingham, UK : Packt Publishing
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations.
    Serie: Learning path
    Schlagwort(e): Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Leverage benefits of machine learning techniques using Python. About This Book Improve and optimise machine learning systems using effective strategies. Develop a strategy to deal with a large amount of data. Use of Python code for implementing a range of machine learning algorithms and techniques. Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. What You Will Learn Learn to write clean and elegant Python code that will optimize the strength of your algorithms Uncover hidden patterns and structures in data with clustering Improve accuracy and consistency of results using powerful feature engineering techniques Gain practical and theoretical understanding of cutting-edge deep learning algorithms Solve unique tasks by building models Get grips on the machine learning design process In Detail Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it's time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resource...
    Anmerkung: Authors: Sebastian Raschka, David Julian, John Hearty. Cf. Credits page. - Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed October 3, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781787128576/?ar
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  • 6
    Online-Ressource
    Online-Ressource
    R : unleash machine learning techniques : find out how to build smarter machine learning systems with R : follow this three module course to become a more fluent machine learning practitioner : a course in three modules (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787128286 , 1787128288
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations.
    Serie: Learning path
    Schlagwort(e): Machine learning ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn ? apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it? This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important ...
    Anmerkung: Authors: Raghav Bali, Dipanjan Sarkar, Brett Lantz, Cory Lesmeister. Cf. Credits page. - Includes bibliographical references and index. - Description based on online resource; title from cover (Safari, viewed November 4, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781787127340/?ar
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  • 7
    Online-Ressource
    Online-Ressource
    Scala : applied machine learning : leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features : a course in three modules (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787124554 , 178712455X
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Schlagwort(e): Scala (Computer program language) ; Machine learning ; Electronic data processing ; Electronic books ; Electronic books ; local
    Kurzfassung: Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features About This Book Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalability and maintainability Who This Book Is For This Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning. What You Will Learn Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to perform technical analysis of financial markets Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail This Learning Path aims to put the entire world of machine learning with Scala in front of you. Scala for Data Science, the first module in this course, is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed building data science and data engineering solutions. The second course, Scala for Machine Learning guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial. The next module, Mastering Scala Machine Learning, is the final step in this course. It will take yo...
    Anmerkung: Authors: Pascal Bugnion, Patrick R. Nicolas, Alex Kozlov. Cf. Credits page. - Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed March 22, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787126640/?ar
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  • 8
    Online-Ressource
    Online-Ressource
    Machine learning for the web : explore the web and make smarter predictions using Python (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 1785888722 , 9781785888724
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Schlagwort(e): Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Explore the web and make smarter predictions using Python About This Book Targets two big and prominent markets where sophisticated web apps are of need and importance. Practical examples of building machine learning web application, which are easy to follow and replicate. A comprehensive tutorial on Python libraries and frameworks to get you up and started. Who This Book Is For The book is aimed at upcoming and new data scientists who have little experience with machine learning or users who are interested in and are working on developing smart (predictive) web applications. Knowledge of Django would be beneficial. The reader is expected to have a background in Python programming and good knowledge of statistics. What You Will Learn Get familiar with the fundamental concepts and some of the jargons used in the machine learning community Use tools and techniques to mine data from websites Grasp the core concepts of Django framework Get to know the most useful clustering and classification techniques and implement them in Python Acquire all the necessary knowledge to build a web application with Django Successfully build and deploy a movie recommendation system application using the Django framework in Python In Detail Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python's impressive Django framework and will find out how to build a modern simple web app with machine learning features. Style and approach Instead of being overwhelmed with multiple concepts at once, this book provides a step-by-step approach that will guide you through one topic at a time. An intuitive step-by step guide that will focus on one key topic at a time. Building upon the acquired knowledge in each chapter, we will connect the fu...
    Anmerkung: Includes index. - Description based on online resource; title from cover (viewed August 29, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785886607/?ar
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  • 9
    Online-Ressource
    Online-Ressource
    Spark for data science : analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785884771 , 1785884778
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Schlagwort(e): Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Machine learning ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 About This Book Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world problems with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you! What You Will Learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RDD, DataFrame and Dataset APIs Become an expert by improving your data analytical skills In Detail This is the era of Big Data. The words ?Big Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects. Style and approach This book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.
    Anmerkung: Description based on online resource; title from cover (Safari, viewed October 20, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785885655/?ar
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  • 10
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    Online-Ressource
    R : recipes for analysis, visualization and machine learning : get savvy with R language and actualize projects aimed at analysis, visualization and machine learning (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787288799 , 178728879X
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Schlagwort(e): R (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Kurzfassung: Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning About This Book Proficiently analyze data and apply machine learning techniques Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R Construct a predictive model by using a variety of machine learning packages Who This Book Is For This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R ? it will help you increase your R expertise, regardless of your level of experience. What You Will Learn Get data into your R environment and prepare it for analysis Perform exploratory data analyses and generate meaningful visualizations of the data Generate various plots in R using the basic R plotting techniques Create presentations and learn the basics of creating apps in R for your audience Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm Build, tune, and evaluate predictive models with different machine learning packages Incorporate R and Hadoop to solve machine learning problems on big data In Detail The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis ? this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data ? this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning ? this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: R Dat...
    Anmerkung: Description based on online resource; title from cover (Safari, viewed December 15, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781787289598/?ar
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  • 11
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    Online-Ressource
    Python : real world machine learning : learn to solve challenging data science problems by building powerful machine learning models using Python (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787120679 , 1787120678
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Schlagwort(e): Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Kurzfassung: Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and fea...
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from cover (Safari, viewed December 6, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781787123212/?ar
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  • 12
    Online-Ressource
    Online-Ressource
    Mastering Scala machine learning : advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785885266 , 178588526X
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Serie: Community experience distilled
    Schlagwort(e): Scala (Computer program language) ; Machine learning ; Electronic data processing ; Electronic books ; Electronic books ; local
    Kurzfassung: Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop About This Book This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability Who This Book Is For Mastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required. What You Will Learn Sharpen your functional programming skills in Scala using REPL Apply standard and advanced machine learning techniques using Scala Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex...
    Anmerkung: Includes index. - Description based on online resource; title from cover (Safari, viewed July 13, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785880889/?ar
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  • 13
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    Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques = Tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784394011 , 1784394017
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Serie: Community experience distilled
    Paralleltitel: Erscheint auch als Gollapudi, Sunila Practical machine learning
    DDC: 006.31
    Schlagwort(e): COMPUTERS / General ; Machine learning ; Machine learning ; Künstliche Intelligenz ; Künstliche Intelligenz
    Beschreibung / Inhaltsverzeichnis: Annotation
    Anmerkung: Description based on online resource; title from cover (Safari, viewed February 12, 2016). - Includes index
    URL: https://learning.oreilly.com/library/view/-/9781784399689/?ar
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  • 14
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    Python machine learning cookbook : 100 recipes that teach you how to perform various machine learning tasks in the real world (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781786467683 , 1786467682
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations.
    Serie: Quick answers to common problems
    Schlagwort(e): Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Kurzfassung: 100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach ...
    Anmerkung: Includes index. - Description based on online resource; title from cover (Safari, viewed July 12, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781786464477/?ar
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  • 15
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    Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785881190 , 1785881191
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Serie: Community experience distilled
    Schlagwort(e): Machine learning ; Microsoft .NET Framework ; Electronic books ; Electronic books ; local
    Kurzfassung: Master the art of machine learning with .NET and gain insight into real-world applications About This Book Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 Set up your business application to start using machine learning techniques Familiarize the user with some of the more common .NET libraries for machine learning Implement several common machine learning techniques Evaluate, optimize and adjust machine learning models Who This Book Is For This book is targeted at .Net developers who want to build complex machine learning systems. Some basic understanding of data science is required. What You Will Learn Write your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0 Set up your business application to start using machine learning. Accurately predict the future using regressions. Discover hidden patterns using decision trees. Acquire, prepare, and combine datasets to drive insights. Optimize business throughput using Bayes Classifier. Discover (more) hidden patterns using KNN and Naïve Bayes. Discover (even more) hidden patterns using K-Means and PCA. Use Neural Networks to improve business decision making while using the latest ASP.NET technologies. Explore ?Big Data?, distributed computing, and how to deploy machine learning models to IoT devices ? making machines self-learning and adapting Along the way, learn about Open Data, Bing maps, and MBrace In Detail .Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions. You will learn what is open data and the awesomeness of type providers...
    Anmerkung: Includes index. - Description based on online resource; title from cover (viewed April 11, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785888403/?ar
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  • 16
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    Java deep learning essentials : dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785283147 , 1785283146
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations.
    Serie: Community experience distilled
    Schlagwort(e): Java (Computer program language) ; Machine learning ; Application software ; Development ; Electronic books ; Electronic books ; local
    Kurzfassung: Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java About This Book Go beyond the theory and put Deep Learning into practice with Java Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe Whether you're a data scientist or Java developer, dive in and find out how to tackle Deep Learning Who This Book Is For This book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment. What You Will Learn Get a practical deep dive into machine learning and deep learning algorithms Implement machine learning algorithms related to deep learning Explore neural networks using some of the most popular Deep Learning frameworks Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Gain an insight into the deep learning library DL4J and its practical uses Get to know device strategies to use deep learning algorithms and libraries in the real world Explore deep learning further with Theano and Caffe In Detail AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries ? as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science ? if you're a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning ...
    Anmerkung: Includes index. - Description based on online resource; title from cover (Safari, viewed June 22, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785282195/?ar
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