I've created this page in order to centralize all of the useful links, book, course or content pertaining to Machine Learning and AI that I've posted in my newsletter!
Feel free to bookmark this page as it will grow week over week with useful content! If you have a submission for some content you think would be a good fit here, don't hesitate to shoot me an email at email@example.com
The Elements of Statistical Learning By Astie et al.
A classic in the machine learning community, highly recommend spending time with this book over and over again. I wouldn't start my machine learning journey with this, but this is the book to ensure your foundation is solid! A lot of questions can be cleared out throughout your machine learning career just by reading that book!
Artificial Intelligence, A Modern Approach (3rd Edition)
This will give you a good overview about the broader field of artificial intelligence which doesn't necessarily include machine learning. I would say this is the kind of book that enable you to start being creative with machine-learning and AI since it shows you all the scaffolding of the field!
The Deep Learning Book
Big classic in the deep learning field, it's surprisingly a very approachable book if you have some background in linear algebra (there is a primer at the beginning). I would go through it at least once in order to see the reasoning behind deep neural network straight from experts in the field; Ian Goodfellow, Yoshua Bengio and Aaron Courville.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
If you had one book to go through to get up and running in the machine learning field, it would be this one. Very thorough and hands-on. I would go for this one before anything else if I were starting learning machine learning with no prior background.
Deep Learning with Pytorch
I love this book because you get a direct insight by the creators of Pytorch about their philosophy (and there are some neat neuroscience examples in there which I'm always happy to see). It explain beautifully not only the theory behind deep neural network, but also how to use Pytorch effectively!