Free Online Books – Machine Learning with Python

Python data science

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)
Python data science handbook
Fig 1. Python data science handbook

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

Building machine learning systems with Python
Fig 2. Building machine learning systems with Python

Deep Learning with Python

Covers Python source code for the following topics:

Deep learning with Python
Fig 3. Deep learning with Python

Natural Language Processing (NLP) with Python

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

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)
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin and Twitter.
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