Free Online Books – Machine Learning with Python

This post lists down free online books for machine learning with Python. These books covers topiccs related to machine learning, deep learning, and NLP. This post will be updated from time to time as I discover more books. 

Here are the titles of these books:

• Python data science handbook
• Building machine learning systems with Python
• Deep learning with Python
• Natural language processing with Python
• Think Bayes
• Scikit-learn tutorial – statistical learning for scientific data processing

Python Data Science Handbook

Covers topics such as some of the following:

• Introduction to Numpy
• Data manipulation with Pandas
• Visualization with Matplotlib
• Machine learning topics (Linear regression, SVM, random forest, principal component analysis, K-means clustering, Gaussian mixture models, Kernel density estimation etc)

Building Machine Learning systems with Python

Covers different topics with Python examples including Numpy/scipy basics, regression (recommendation), classification (classification problems, sentiment analysis, music genre classification) and clustering (topic modelling, finding related posts), computer vision (pattern recognition), dimensionality reduction

Deep Learning with Python

Covers Python source code for the following topics:

• Chapter 2: The mathematical building blocks of neural networks
• Chapter 3: Introduction to Keras and TensorFlow
• Chapter 4: Getting started with neural networks: classification and regression
• Chapter 5: Fundamentals of machine learning
• Chapter 7: Working with Keras: a deep dive
• Chapter 8: Introduction to deep learning for computer vision
• Chapter 9: Advanced deep learning for computer vision
  • Part 1: Image segmentation
  • Part 2: Modern convnet architecture patterns
  • Part 3: Interpreting what convnets learn
• Chapter 10: Deep learning for timeseries
• Chapter 11: Deep learning for text
  • Part 1: Introduction
  • Part 2: Sequence models
  • Part 3: Transformer
  • Part 4: Sequence-to-sequence learning
• Chapter 12: Generative deep learning
  • Part 1: Text generation
  • Part 2: Deep Dream
  • Part 3: Neural style transfer
  • Part 4: Variational autoencoders
  • Part 5: Generative adversarial networks

Natural Language Processing (NLP) with Python

Cover different topics such as the following while utilizing natural language toolkit (NLTK)

• Language Processing and Python
• Accessing Text Corpora and Lexical Resources
• Processing Raw Text
• Writing Structured Programs
• Categorizing and Tagging Words
• Learning to Classify Text
• Extracting Information from Text
• Analyzing Sentence Structure
• Building Feature Based Grammars
• Analyzing the Meaning of Sentences
• Managing Linguistic Data
• Afterword: Facing the Language Challenge

Think Bayes

Covers topics related to Bayesian statistics using computational methods.

• Bayes theorem
• Estimating proportions & counts
• Poisson process
• Decision analysis
• Survival analysis

Scikit-learn Tutorial – Statistical Learning for Scientific Data Processing

Covers different topics such as the following:

• Statistical learning: the setting and the estimator object
• Supervised learning: Making predictions based on high-dimensional observations
• Model selection (cross-validation generators, grid search)
• Unsupervised learning (clustering, decompositions)