Link to the GitHub Repository containing the code examples and additionalmaterial: [ of the most innovative breakthroughs and exciting new technologies can beattributed to applications of machine learning. We are living in an age wheredata comes in abundance, and thanks to the self-learning algorithms from thefield of machine learning, we can turn this data into knowledge. Automatedspeech recognition on our smart phones, web search engines, e-mail spamfilters, the recommendation systems of our favorite movie streaming services –machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed inrecent years, machine learning is now right at our fingertips. Python providesthe perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how toutilize these in real-world applications using Python. Step-by-step, you willexpand your skill set with the best practices for transforming raw data intouseful information, developing learning algorithms efficiently, and evaluatingresults.You will discover the different problem categories that machine learning cansolve and explore how to classify objects, predict continuous outcomes withregression analysis, and find hidden structures in data via clustering. Youwill build your own machine learning system for sentiment analysis andfinally, learn how to embed your model into a web app to share with the worldwords : 141799

trantor/en/Mirjalili, Vahid/Python Machine Learning · 2nd Edition.epub

zlib/no-category/Sebastian Raschka, Vahid Mirjalili/Python Machine Learning · 2nd Edition_30534484.epub

Python Machine Learning - Second Edition : Unlock Modern Machine Learning and Deep Learning Techniques with Python by Using the Latest Cutting-edge Open Source Python Libraries.

Python Machine Learning : Learn How to Build Powerful Python Machine Learning Algorithms to Generate Useful Data Insights with This Data Analysis Tutorial

Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Python и машинное обучение: наука и искусство построения алгоритмов, которые извлекают знания из данных

Python Machine Learning, 1st Edition

Себастьян Рашка; перевод с англ. А. В. Логунова

Raschka, Sebastian, Mirjalili, Vahid

Mirjalili, Vahid; Raschka, Sebastian

Рашка, Себастьян

ДМК Пресс

Community experience distilled, Community experience distilled, England, 2016

Second edition, Fully revised and updated, Birmingham, 2017

Community experience distilled, Birmingham, UK, 2015

United Kingdom and Ireland, United Kingdom

Packt Publishing, Birmingham, UK, 2015

Цветное издание, Москва, Russia, 2017

Packt Publishing, Birmingham, 2017

1st edition, 2015

2nd edition, 2017

Sep 20, 2017

Includes index.

gaaagpl

Предм. указ.: с. 408-417
Пер.: Raschka, Sebastian Python machine learning Birmingham ; Mumbai : Packt, cop. 2016 978-1-78355-513-0

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Key Features A practical approach to the frameworks of data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningLearn best practices to improve and optimize your machine learning systems and algorithms Book Description Machine learning is eating the software world, and now deep learning is extending machine learning. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python. This Second Edition of Sebastian Raschka's Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning. Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning. Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. Readers of the first edition will be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning. What you will learn Use the key frameworks of data science, machine learning, and deep learningAsk new questions of your data through machine learning models and neural networksWork with the most powerful Python open-source libraries in machine learningBuild deep learning applications using Keras and TensorFlowEmbed your machine learning model in accessible web applicationsPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringAnalyze images using deep learning techniquesUse sentiment analysis to delve deeper into textual and social media data About the Author Sebastian Raschka, author of the best selling Python Machine Learning, has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning, including a machine learning tutorial at SciPy, the leading conference for scientific computing in Python. Sebastian loves to write and talk about data science, machine learning, and Python, and he's motivated to help people developing data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on nove

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.About This BookSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize your machine learning systems and algorithmsWho This Book Is ForIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.What You Will LearnUnderstand the key frameworks in data science, machine learning, and deep learningHarness the power of the latest Python open source libraries in machine learningExplore machine learning techniques using challenging real-world dataMaster deep neural network implementation using the TensorFlow libraryLearn the mechanics of classification algorithms to implement the best tool for the jobPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysisIn DetailMachine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.Style and ApproachPython Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.

<p>Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics<br></p><p>About This Book<br></p><ul> <li>Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization </li> <li>Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms </li> <li>Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets </li></ul><p>Who This Book Is For<br></p><p>If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.<br></p><p>What You Will Learn<br></p><ul> <li>Explore how to use different machine learning models to ask different questions of your data </li> <li>Learn how to build neural networks using Keras and Theano </li> <li>Find out how to write clean and elegant Python code that will optimize the strength of your algorithms </li> <li>Discover how to embed your machine learning model in a web application for increased accessibility </li> <li>Predict continuous target outcomes using regression analysis </li> <li>Uncover hidden patterns and structures in data with clustering </li> <li>Organize data using effective pre-processing techniques </li> <li>Get to grips with sentiment analysis to delve deeper into textual and social media data </li></ul><p>In Detail<br></p><p>Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.<br></p><p>Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.<br></p><p>Style and approach<br></p><p>Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.<br></p>

Unlock Modern Machine Learning And Deep Learning Techniques With Python By Using The Latest Cutting-edge Open Source Python Libraries. About This Book * Second Edition Of The Bestselling Book On Machine Learning * A Practical Approach To Key Frameworks In Data Science, Machine Learning, And Deep Learning * Use The Most Powerful Python Libraries To Implement Machine Learning And Deep Learning * Get To Know The Best Practices To Improve And Optimize Your Machine Learning Systems And Algorithms Who This Book Is For If You Know Some Python And You Want To Use Machine Learning And Deep Learning, Pick Up This Book. Whether You Want To Start From Scratch Or Extend Your Machine Learning Knowledge, This Is An Essential And Unmissable Resource. Written For Developers And Data Scientists Who Want To Create Practical Machine Learning And Deep Learning Code, This Book Is Ideal For Developers And Data Scientists Who Want To Teach Computers How To Learn From Data. What You Will Learn * Understand The Key Frameworks In Data Science, Machine Learning, And Deep Learning * Harness The Power Of The Latest Python Open Source Libraries In Machine Learning * Explore Machine Learning Techniques Using Challenging Real-world Data * Master Deep Neural Network Implementation Using The Tensorflow Library * Learn The Mechanics Of Classification Algorithms To Implement The Best Tool For The Job * Predict Continuous Target Outcomes Using Regression Analysis * Uncover Hidden Patterns And Structures In Data With Clustering * Delve Deeper Into Textual And Social Media Data Using Sentiment Analysis In Detail Machine Learning Is Eating The Software World, And Now Deep Learning Is Extending Machine Learning. Understand And Work At The Cutting Edge Of Machine Learning, Neural Networks, And Deep Learning With This Second Edition Of Sebastian Raschka's Bestselling Book, Python Machine Learning. --publisher's Description. 1. Giving Computers The Ability To Learn From Data -- 2. Training Simple Machine Learning Algorithms For Classification -- 3. A Tour Of Machine Learning Classifiers Using Scikit-learn -- 4. Building Good Training Sets-data Preprocessing -- 5. Compressing Data Via Dimensionality Reduction -- 6. Learning Best Practices For Model Evaluation And Hyperpaarmeter Tuning -- 7.combining Different Models For Ensemble Learning -- 8. Applying Machine Learning To Sentiment Analysis -- 9. Embedding A Machine Learning Model Into A Web Application -- 10. Predicting Continuous Target Variables With Regression Analysis -- 11. Working With Unlabeled Data-clustering Analysis -- 12. Implementing A Multilayer Artificial Neural Network From Scratch -- 13. Parallelizing Neural Network Training With Tensorflow -- 14. Going Deeper -- The Mechanics Of Tensorflow -- 15. Classifying Images With Deep Convolutional Neural Networks -- 16. Modeling Sequential Data Using Recurrent Neural Networks. Sebastian Raschka, Vahid Mirajalili. Includes Index.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsKey FeaturesLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasetsBook DescriptionMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. What you will learnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataWho this book is forIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask -- and answer -- tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning -- whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Pylearn 2 and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data -- its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer som..

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from s..

Link to the GitHub Repository containing the code examples and additional
material: [https://github.com/rasbt/python-machi...](https://github.com/rasbt
/python-machine-learning-book)
Many of the most innovative breakthroughs and exciting new technologies can be
attributed to applications of machine learning. We are living in an age where
data comes in abundance, and thanks to the self-learning algorithms from the
field of machine learning, we can turn this data into knowledge. Automated
speech recognition on our smart phones, web search engines, e-mail spam
filters, the recommendation systems of our favorite movie streaming services –
machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in
recent years, machine learning is now right at our fingertips. Python provides
the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to
utilize these in real-world applications using Python. Step-by-step, you will
expand your skill set with the best practices for transforming raw data into
useful information, developing learning algorithms efficiently, and evaluating
results.
You will discover the different problem categories that machine learning can
solve and explore how to classify objects, predict continuous outcomes with
regression analysis, and find hidden structures in data via clustering. You
will build your own machine learning system for sentiment analysis and
finally, learn how to embed your model into a web app to share with the world
Programming,python

Link to the GitHub Repository containing the code examples and additional material: (https://github.com/rasbt/python-machine-learning-book) https://github.com/rasbt/python-machi...
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

"Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn."-- Résumé de l'éditeur

Giving computers the ability to learn from data
Training machine learning algorithms for classification
A tour of machine learning classifiers using Scikit-learn
Building good training sets : data preprocessing
Compressing data via dimensionality reduction
Learning best practices for model evaluation and hyperparameter tuning
Combining different models for ensemble learning
Applying machine learning to sentiment analysis
Embedding a machine learning model into a web application
Predicting continuous target variables with regression analysis
Working with unlabeled data : clustering analysis
Training artificial neural networks for image recognition
Parallelizing neural network training with Theano.

2024-06-27