Following are some of the books that I have read/currently reading on Machine Learning and I found them very helpful:
- Deep Learning with Python by François Chollet
- Deep Learning by Ian Goodfellow, Yoshua Bengio & Aaron Courville
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
- Machine Learning Interviews by Chip Huyen
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen
- Machine Learning: A Probabilistic Perspective by Kevin Murphy (Must Read! The author has made available some sequels to the book meant for targeted audience recently; they can be found here: GitHub)
- Machine Learning CSS 229 - Stanford Online
- Advanced Machine Learning CS711 - CMU Online
- Probabilistic Machine Learning ML4202 - University of Tübingen
- Neural Networks: Zero to Hero - Andrej Karpathy
- Machine Learning with Graphs CS224W - Stanford Online
- Deep Learning CS230 - Stanford Online
I recently made a list of topics meant for revision:
(Target audience is someone who is interviewing for a ML Engineer Role or a NLP Engineer role)
Referred course material is from Stanford CS 229 Revision Notes and 100 Page ML Book by Andriy Burkov.
Also, interview practice questions with ChatGPT can be very helpful too. LOL.
If you are someone who is interested in reading selected papers from different areas of Machine Learning, please go through this page curated by Aman Chadha. He also posts some awesome tutorials on his blog.