Python Machine Learning code repository.
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.
Paperback: 454 pages
Publisher: Packt Publishing
Language: English
ISBN-10: 1783555130
ISBN-13: 978-1783555130
Kindle ASIN: B00YSILNL0
Sebastian Raschka’s new book, Python Machine Learning, has just been released. I got a chance to read a review copy and it’s just as I expected - really great! It’s well organized, super easy to follow, and it not only offers a good foundation for smart, non-experts, practitioners will get some ideas and learn new tricks here as well.
– Lon Riesberg at Data Elixir
Superb job! Thus far, for me it seems to have hit the right balance of theory and practice…math and code!
– Brian Thomas
I've read (virtually) every Machine Learning title based around Scikit-learn and this is hands-down the best one out there.
– Jason Wolosonovich
- ebook and paperback at Amazon.com, Amazon.co.uk, Amazon.de
- ebook and paperback from Packt (the publisher)
- at other book stores: O'Reilly, Safari, Barnes & Noble, Apple iBooks, ...
- free sample chapter
Simply click on the ipynb/nbviewer links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version).
Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.
Excerpts from the Foreword and Preface
- Machine Learning - Giving Computers the Ability to Learn from Data [dir] [ipynb] [nbviewer]
- Training Machine Learning Algorithms for Classification [dir] [ipynb] [nbviewer]
- A Tour of Machine Learning Classifiers Using Scikit-Learn [dir] [ipynb] [nbviewer]
- Building Good Training Sets – Data Pre-Processing [dir] [ipynb] [nbviewer]
- Compressing Data via Dimensionality Reduction [dir] [ipynb] [nbviewer]
- Learning Best Practices for Model Evaluation and Hyperparameter Optimization [dir] [ipynb] [nbviewer]
- Combining Different Models for Ensemble Learning [dir] [ipynb] [nbviewer]
- Applying Machine Learning to Sentiment Analysis [dir] [ipynb] [nbviewer]
- Embedding a Machine Learning Model into a Web Application [dir] [ipynb] [nbviewer]
- Predicting Continuous Target Variables with Regression Analysis [dir] [ipynb] [nbviewer]
- Working with Unlabeled Data – Clustering Analysis [dir] [ipynb] [nbviewer]
- Training Artificial Neural Networks for Image Recognition [dir] [ipynb] [nbviewer]
- Parallelizing Neural Network Training via Theano [dir] [ipynb] [nbviewer]
Bonus Notebooks (not in the book)
- A Basic Pipeline and Grid Search Setup [dir] [ipynb] [nbviewer]
- An Extended Nested Cross-Validation Example [dir] [ipynb] [nbviewer]
- Why do you and other people sometimes implement machine learning algorithms from scratch?
- What learning path/discipline in data science I should focus on?
- At what point should one start contributing to open source?
- How important do you think having a mentor is to the learning process?
- Where are the best online communities centered around data science/machine learning or python?
- How would you explain machine learning to a software engineer?
- How would your curriculum for a machine learning beginner look like?
- What are some real-world examples of applications of machine learning in the field?
- What are the different fields of study in data mining?
- What are differences in research nature between the two fields: machine learning & data mining?
- How do I know if the problem is solvable through machine learning?
- What are the origins of machine learning?
- How was classification, as a learning machine, developed?
- Which machine learning algorithms can be considered as among the best?
- What are the broad categories of classifiers?
- What is the difference between a classifier and a model?
- Fitting a model via closed-form equations vs. Gradient Descent vs Stochastic Gradient Descent vs Mini-Batch Learning -- what is the difference?
- How do you derive the Gradient Descent rule for Linear Regression and Adaline?
- How does the random forest model work? How is it different from bagging and boosting in ensemble models?
- What are the disadvantages of using classic decision tree algorithm for a large dataset?
- Why are implementations of decision tree algorithms usually binary, and what are the advantages of the different impurity metrics?
- Why are we growing decision trees via entropy instead of the classification error?
- Is it always better to have the largest possible number of folds when performing cross validation?
- When training an SVM classifier, is it better to have a large or small number of support vectors?
- How do I evaluate a model?
- What is the best validation metric for multi-class classification?
- What factors should I consider when choosing a predictive model technique?
- What is the probabilistic interpretation of regularized logistic regression?
- Does regularization in logistic regression always results in better fit and better generalization?
- What is the major difference between naive Bayes and logistic regression?
- What exactly is the "softmax and the multinomial logistic loss" in the context of machine learning?
- What is the relation between Loigistic Regression and Neural Networks and when to use which?
- What is the difference between deep learning and usual machine learning?
- Can you give a visual explanation for the back propagation algorithm for neural networks?
- Why did it take so long for deep networks to be invented?
- What are some good books/papers for learning deep learning?
- Why are there so many deep learning libraries?
- Why do some people hate neural networks/deep learning?
- What are the different dimensionality reduction methods in machine learning?
- What is the difference between LDA and PCA for dimensionality reduction?
- When should I apply data normalization/standardization?
- Does mean centering or feature scaling affect a Principal Component Analysis?
- How do you attack a machine learning problem with a large number of features?
- Why is the Naive Bayes Classifier naive?
- What is the decision boundary for Naive Bayes?
- Can I use Naive Bayes classifiers for mixed variable types?
- Is it possible to mix different variable types in Naive Bayes, for example, binary and continues features?
- What is Euclidean distance in terms of machine learning?
- When should one use median, as opposed to the mean or average?
- Can I use paragraphs and images from the book in presentations or my blog?
- How is this different from other machine learning books?
- Which version of Python was used in the code examples?
- Which technologies and libraries are being used?
- Which book version/format would you recommend?
- Why did you choose Python for machine learning?
- Why do you use so many leading and trailing underscores in the code examples?
- Are there any prerequisites and recommended pre-readings?
I am happy to answer questions! Just write me an email or consider asking the question on the Google Groups Email List.
If you are interested in keeping in touch, I have quite a lively twitter stream (@rasbt) all about data science and machine learning. I also maintain a blog where I post all of the things I am particularly excited about.