Learning Resources
List of Resources
There is a nearly endless list of resources for learning deep learning and machine learning more broadly. I've included some below, with my favorites marked by a 
.
.Recommendations
Currently, my personal recommendation would be to do the following:
For classical machine learning...
Read Chapters 1-2 of Hands-On Machine Learning with Scikit-Learn.
If you have a relatively small amount of data where deep learning will not be viable, then stick with scikit-learn and skip the section below. Start tinkering.
For neural networks and deep learning...
For a theoretical understanding of deep learning, refer to Understanding Deep Learning as needed. It will provide a solid foundation, without being overly technical. Chapters 1-10 and 13 are particularly valuable within the context of our group.
For a practical understanding, go through the PyTorch documentation, which itself has several tutorials. Start tinkering with PyTorch on your own.
Machine Learning Fundamentals
Practical
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow This is by far my top-choice recommendation for learning about scikit-learn and more "classical" machine learning topics. That said, the latter half (i.e. the Keras and TensorFlow sections) is probably worth skipping in favor of other options. Chapters 1-4 are a perfect intro.
Theoretical
Probabilistic Machine Learning: An Introduction
This takes a very statistics-oriented approach to machine learning. It is a modern classic but a bit on the more math-intensive side.
Deep Learning
Practical
This is a rather useful and interactive book on deep learning with PyTorch, MXNet, JAX, and TensorFlow. I don't recommend reading it front to back, but it's a useful reference for specific topics.
Deep Learning with Pytorch
This is an extremely useful intro book to PyTorch for deep learning.
This is a useful video-based tutorial for PyTorch.
Theoretical
This is definitely my favorite introductory book about deep learning. It strikes the perfect balance and provides the necessary context to understand what you will later be coding.
Geometric Deep Learning ( https://geometricdeeplearning.com/book )
Deep Learning by Goodfellow, Bengio, and Courville
This is basically the standard deep learning book, but it's a bit on the math-intensive side and also missing newer topics.
Probabilistic Machine Learning: Advanced Topics
This is essentially the deep learning analogue of Probabilistic Machine Learning: An Introduction.
Applications
The following are useful resources are domain-specific applications for machine learning as it relates to quantum chemistry and computational materials science:
Quantum Chemistry in the Age of Machine Learning
Quantum Chemistry in the Age of Machine LearningIf you want to get a feel for what others have done in the field as a whole, this is an excellent point of reference. It's one of the few resources that combines ML and DFT.
The examples are tailored for chemistry and materials science, which is nice. That said, the examples aren't written with PyTorch, which is predominantly what I recommend using.
Machine Learning Meets Quantum Physics
This is a useful reference book on several important machine learning topics. The chapters on kernel methods and approaches like SOAP are particularly accessible.
AI-Guided Design and Property Prediction for Zeolites and Nanoporous Materials
This is a useful reference of prior work in our particular field of research field. The field is moving fast though so will likely quickly become outdated, but there are some nice examples.
This is a nice review article, especially the first half or so that is focused on descriptors and methodology, which is tailored towards porous materials. The applications will rapidly become outdated though.