Getting Started in Machine Learning
 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).
 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.
Recommended Book:
HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition, by Aurélien Géron , and its notebooks.
Other wellknown references for statistical background could be used to understand the statistical theories and proofs If needed.
Fundamental background (prerequisites):
 Linear Algebra
 Calculus
 Probabilities and statistics
 Basic programming
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.
 For Fundamentals

If you prefer courses, the goldstandard is: Machine Learning, by Andrew Ng, Stanford
 Must take for ML basics
 Viewed by more than 8M students, 2.7M enrolled in the new system
 Theory, languageindependent in general (but examples in Octave, a Matlablike language)

If you prefer Textbooks, the free book: An Introduction to Statistical Learning
 Udemy, ~$20
 500k+ students took the course (4.5 stars rating)
 41 hrs, handson projects
 Theory and practical Python for Data Science and Machine Learning Bootcamp
 Udemy, ~$20
 21 hrs
 Practical  Excellent instructor
 Not deep in fundamentals, but good handson
Neural Networks and Deep learning:
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
 Udemy
 22 hrs
 Practical, explains the basics,
 but NO mathematics
 Coursera (free without certification)
 From Andrew Ng Deep.ai
 Theory and how to use → experience
 Can be taken for free (if Audit), or pay to get a certificate
 Part of a full 5 courses specialization (RNN,hyperparameters opt, ...etc)
 Part of the above specialization
Very comprehensive articles: (not to replace courses)
 Fundamental Techniques of Feature Engineering for Machine Learning (excellent)
 Comparative Study on Classic Machine learning Algorithms
 ML Notebooks
 Which Machine Learning Algorithm Should You Use By Problem Type
 A guide to an efficient way to build neural network architectures Part I: Hyperparameter selection and tuning for Dense Networks using Hyper as on FashionMNIST
 Google, Rules of Machine Learning
 Google, Machine Learning Crush Course
 Tinker With a Neural Network Right Here in Your Browser
 Comparative Study on Classic Machine learning Algorithms
 Tutorials with ML use in QM