开源软件名称(OpenSource Name):rasbt/python-machine-learning-book开源软件地址(OpenSource Url):https://github.com/rasbt/python-machine-learning-book开源编程语言(OpenSource Language):Jupyter Notebook 99.0%开源软件介绍(OpenSource Introduction):Python Machine Learning book code repositoryIMPORTANT NOTE (09/21/2017):This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano. You are not sure if this book is for you? Please checkout the excerpts from the Foreword and Preface, or take a look at the FAQ section for further information. 1st edition, published September 23rd 2015 German ISBN-13: 978-3958454224 Table of Contents and Code NotebooksSimply click on the
Equation ReferenceSlides for TeachingA big thanks to Dmitriy Dligach for sharing his slides from his machine learning course that is currently offered at Loyola University Chicago. Additional Math and NumPy ResourcesSome readers were asking about Math and NumPy primers, since they were not included due to length limitations. However, I recently put together such resources for another book, but I made these chapters freely available online in hope that they also serve as helpful background material for this book:
Citing this BookYou are very welcome to re-use the code snippets or other contents from this book in scientific publications and other works; in this case, I would appreciate citations to the original source: BibTeX:
MLA: Raschka, Sebastian. Python machine learning. Birmingham, UK: Packt Publishing, 2015. Print. Feedback & ReviewsShort review snippets
Longer reviewsIf you need help to decide whether this book is for you, check out some of the "longer" reviews linked below. (If you wrote a review, please let me know, and I'd be happy to add it to the list).
Links
Translations
Literature References & Further Reading ResourcesErrataBonus Notebooks (not in the book)
"Related Content" (not in the book)
SciPy 2016We had such a great time at SciPy 2016 in Austin! It was a real pleasure to meet and chat with so many readers of my book. Thanks so much for all the nice words and feedback! And in case you missed it, Andreas Mueller and I gave an Introduction to Machine Learning with Scikit-learn; if you are interested, the video recordings of Part I and Part II are now online! PyData Chicago 2016I attempted the rather challenging task of introducing scikit-learn & machine learning in just 90 minutes at PyData Chicago 2016. The slides and tutorial material are available at "Learning scikit-learn -- An Introduction to Machine Learning in Python." Note I have set up a separate library, TranslationsDear readers, Over the last couple of months, I received hundreds of emails, and I tried to answer as many as possible in the available time I have. To make them useful to other readers as well, I collected many of my answers in the FAQ section (below). In addition, some of you asked me about a platform for readers to discuss the contents of the book. I hope that this would provide an opportunity for you to discuss and share your knowledge with other readers: Google Groups Discussion Board(And I will try my best to answer questions myself if time allows! :))
Examples and Applications by ReadersOnce again, I have to say (big!) THANKS for all the nice feedback about the book. I've received many emails from readers, who put the concepts and examples from this book out into the real world and make good use of them in their projects. In this section, I am starting to gather some of these great applications, and I'd be more than happy to add your project to this list -- just shoot me a quick mail!
FAQGeneral Questions
Questions about the Machine Learning Field
Questions about ML Concepts and StatisticsCost Functions and Optimization
Regression AnalysisTree models
Model evaluation
Logistic Regression
Neural Networks and Deep Learning
Other Algorithms for Supervised LearningUnsupervised LearningSemi-Supervised LearningEnsemble MethodsPreprocessing, Feature Selection and Extraction
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