# Machine Learning Books and Tutorials

## Machine Learning Tutorials

### Open Licensed Machine Learning Tutorials

* [Machine Learning Algorithm](https://github.com/rushter/MLAlgorithms) License: MIT
* [ML from Scratch](https://github.com/jarfa/ML_from_scratch/) License: Apache
* [ML from Scratch](https://github.com/eriklindernoren/ML-From-Scratch) License: MIT
* [Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning) License: MIT
* [Machine Learning Experiments](https://github.com/trekhleb/machine-learning-experiments) License: MIT

### Free Machine Learning Tutorials

* [NTLK](https://www.nltk.org/book/) License: CC-BY-NC-ND (Note: consider replace by Gensim or Spacy)
* [Ipyton Notebooks of Andrew Ng](https://github.com/jdwittenauer/ipython-notebooks) License: -
* [Machine Learning Course with Python](https://github.com/machinelearningmindset/machine-learning-course) License: -
* Notes for the [Reinforcement Learning course](https://github.com/dalmia/David-Silver-Reinforcement-learning) by David Silver along with implementation of various algorithms.

## Machine Learning Certification at Google

* [Professional ML Engineer Certification - Certifications - Google Cloud](https://cloud.google.com/certification/machine-learning-engineer)
* [Google Cloud skills campaign](https://inthecloud.withgoogle.com/google-cloud-skills/register.html)
* [Baseline: Data, ML, AI - Qwiklabs](https://google.qwiklabs.com/quests/34)
* [Registration - Certifications - Google Cloud](https://cloud.google.com/certification/register)
* [ML Academy Webinar](https://cloudonair.withgoogle.com/events/ml-lab)
* [Qwiklabs - Hands-On Cloud Training](https://google.qwiklabs.com/)
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate - Coursera](https://www.coursera.org/professional-certificates/preparing-for-google-cloud-machine-learning-engineer-professional-certificate)

## Machine Learning Books

### CC-BY Machine Learning Books

* [Automatic ML](https://www.automl.org/book/) License: CC-BY
* [Free and Open Machine Learning](https://freeandopenmachinelearning.readthedocs.io/en/latest/) License: CC-BY
* [Machine Learning Canvas](https://www.louisdorard.com/machine-learning-canvas) License: CC-BY
* [Free and Open Machine Learning Book](https://freeandopenmachinelearning.readthedocs.io/en/latest/) License CC-BY
* [Advanced Applications for Artificial Neural Networks](https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks) License: CC-BY
* [Artificial Neural Networks - Models and Applications](https://www.intechopen.com/books/artificial-neural-networks-models-and-applications) License: CC-BY
* [Artificial Neural Networks - Architectures and Applications](https://www.intechopen.com/books/artificial-neural-networks-models-and-applications) License: CC-BY
* [Artificial Neural Networks - Methodological Advances and Biomedical Applications](https://www.intechopen.com/books/artificial-neural-networks-methodological-advances-and-biomedical-applications) License: CC-BY
* \[Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers] Author : Mariette Awad, Rahul Khanna License: CC-BY

### CC-BY-NC Machine Learning Books

* [Interpretable Machine Learning: A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/) License: CC-BY-NC
* [Pablo Caceres Notes](https://pabloinsente.github.io/archive) License: CC-BY-NC, [Project Web](https://pablocaceres.org/projects/), [Github](https://github.com/pabloinsente) License: MIT

### CC-BY-NC-ND Machine Learning Books

* [Machine Learning, Statistics, and Data Mining for Heliophysics](https://helioml.org/title), [GitHub Link](https://github.com/HelioML/HelioML) License: CC-BY-NC-ND
* [Applied Artificial Neural Networks](https://www.mdpi.com/books/pdfview/book/236) License: CC-BY-NC-ND
* [A Brief Introduction to Neural Networks](https://www.dkriesel.com/en/science/neural_networks) License: CC-BY-NC-ND
* [Reinforcement Learning: An Introduction, Second Edition](https://incompleteideas.net/sutton/book/the-book.html) License : CC-BY-NC-ND
* [Recurrent Neural Networks](https://www.intechopen.com/books/recurrent_neural_networks) License: CC-BY-NC-ND

### Free Machine Learning Books

* [VMLS Book](https://vmls-book.stanford.edu/)
* [Python Machine Learning 2nd Edition Code](https://github.com/rasbt/python-machine-learning-book-2nd-edition), [Python Machine Learning 1st Edition Code](https://github.com/rasbt/python-machine-learning-book), [Slides](https://github.com/dmitriydligach/PyMLSlides)
* [Recurrent Neural Networks and Soft Computing](https://www.intechopen.com/books/recurrent-neural-networks-and-soft-computing)
* [Recurrent Neural Networks for Temporal Data Processing](https://www.intechopen.com/books/recurrent-neural-networks-for-temporal-data-processing)
* [Machine Learning Mastery](https://machinelearningmastery.com/)
* [Mathematics for Machine Learning](https://mml-book.github.io/) with Tutorial
* [Machine Lerning Book](https://www.cs.ubc.ca/~murphyk/MLbook/)
* [abhishekkrthakur/approachingalmost: Approaching (Almost) Any Machine Learning Problem](https://github.com/abhishekkrthakur/approachingalmost)
* [Mathematics for Machine Learning - Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press.](https://mml-book.github.io/)
* [mml-book/mml-book.github.io: Companion webpage to the book "Mathematics For Machine Learning"](https://github.com/mml-book/mml-book.github.io)

### Proprietary Machine Learning Books

* [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html)
* [Machine Learning Engineering](https://www.mlebook.com/wiki/doku.php) by Andriy Burkov

## List of Machine Learning Books

* [List NN Book Open](https://www.freetechbooks.com/neural-networks-f58.html)
* [Free Tech Books](https://www.freetechbooks.com/licenses?page=1)
* [Recommended Books](https://mentorcruise.com/books/ml/)
* [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning)
* [List ML Books](https://www.readthistwice.com/lists/best-machine-learning-books?s=rlearnmachinelearning)
* [Cool Machine Learning Books](https://matpalm.com/blog/cool_machine_learning_books/)
* [Free Machine Learning Resource](https://www.marktechpost.com/free-resources/)

## Machine Learning Notes

* [Archive](https://pabloinsente.github.io/archive) License: CC0
  * [Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)
  * [Introduction to Linear Regression - mathematics and application with Python](https://pabloinsente.github.io/intro-linear-regression)
  * [The Recurrent Neural Network - Theory and Implementation of the Elman Network and LSTM](https://pabloinsente.github.io/the-recurrent-net)
  * [The Convolutional Neural Network - Theory and Implementation of LeNet-5 and AlexNet](https://pabloinsente.github.io/the-convolutional-network)
  * [The Multilayer Perceptron - Theory and Implementation of the Backpropagation Algorithm](https://pabloinsente.github.io/the-multilayer-perceptron)
  * [The ADALINE - Theory and Implementation of the First Neural Network Trained With Gradient Descent](https://pabloinsente.github.io/the-adaline)
  * [The Perceptron - A Guided Tutorial Through Its History and Implementation In Python](https://pabloinsente.github.io/the-perceptron)
  * [The McCulloch-Pitts Artificial Neuron Model - Theory and Implementation](https://pabloinsente.github.io/the-mcculloch-pitts-artificial-neuron-model)

## Machine Learning Seminar

* [Stanford MLSys Seminar Series–Stanford MLSys Seminar Series](https://mlsys.stanford.edu/)

## About ML Books

If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.

If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.

If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.

If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.

If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)

For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code [https://github.com/emredjan/ISL-python](https://github.com/emredjan/ISL-python) ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."

## Transformers

* [Learn Transformers from Scratch](https://e2eml.school/transformers.html)

## Machine Learning Tutorial

* [Scikit Learn](https://inria.github.io/scikit-learn-mooc/) Lincese: CC BY

## Learn Deep Learning

* [Data Science Project Ideas](https://www.theinsaneapp.com/2020/08/data-science-project-ideas-with-source-code.html)

## Machine Learning

* [Fast AI Launch FastAI v2 Library and Free Books](https://www.fast.ai/2020/08/21/fastai2-launch/)
* [Video Face Recognition](https://roundbit.tech/w/video-face-recognition/)
* [Fast AI Book Launch](https://www.fast.ai/2020/08/21/fastai2-launch/)
  * [Course Practical Deep Learning for Coders](https://course.fast.ai/)
  * [GitHub Colab](https://github.com/fastai/fastbook)
  * [Practical Data Ethics](https://ethics.fast.ai/)

## Machine Learning

* [Awesome Production of Machine Learning](https://github.com/EthicalML/awesome-production-machine-learning)
* [Machine Learning Roadmap](https://github.com/mrdbourke/machine-learning-roadmap)
* [Learn ML Effectively](https://www.youtube.com/watch?v=r2X9Se6ayGQ\&feature=share)

## ML Books

* [Book 0: “Machine Learning: A Probabilistic Perspective” (2012) - pml-book](https://probml.github.io/pml-book/)
* [Machine Learning with PyTorch and Scikit-Learn](https://sebastianraschka.com/blog/2022/ml-pytorch-book.html)
