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
- Chapter 10: Deep learning for timeseries
- Chapter 11: Deep learning for text
- Chapter 12: Generative deep learning

## 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)

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