Getting Started in Machine Learning

  1. Build the breadth of ML knowledge by taking a FULL course in ML, ideally a graduate level at STAT/MATH/CS departments. If not available, then take a complete online course (as described in this guide).
  2. Never reply solely on notebooks or online tutorials to learn the basics to avoid the common knowledge gaps. Think of a researcher with zero statistical knowledge trying to learn just from tutorials, surely their published analysis will have serious flaws.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition, by Aurélien Géron , and its notebooks.

Other well-known references for statistical background could be used to understand the statistical theories and proofs If needed.

Fundamental background (prerequisites):

General Machine Learning:

For this section, you can take a Machine learning or data science university course, take an online course, or study a textbook. Here we suggest the online track if university courses are not available.

  1. For Fundamentals
  1. Machine Learning A-Z™: Hands-On Python & R In Data Science
  1. Theory and practical Python for Data Science and Machine Learning Bootcamp

Neural Networks and Deep learning:

  1. Deep Learning for Molecules and Materials

This textbooks covers deep learning for molecules and materials with code examples and theory. It starts from ML and works up to modern methods in deep learning

  1. Deep Learning A-Z™: Hands-On Artificial Neural Networks
  1. Neural Networks and Deep Learning
  1. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization

Very comprehensive articles: (not to replace courses)