Thiago Cardoso :: Machine Learning reading (and watching) list

Reading (and watching) list for getting started on Machine Learning.

Foundation

course Andrew Ng’s Machine Learning Course : A good starting point. Although it is not recent, the course is a good overview of Machine Learning methods.
course Deep Learning Specialization : Andrew’s specialization on Deep Learning methods that hold state of the art in many applications.
course Structuring Machine Learning Projects : A course of the Deep Learning specialization that deserves mention. This is a short course with good practices for creating ML projects.
paper A Few Useful Things to Know About Machine Learning : Paper with interesting thoughts on Machine Learning and our intuition when analyzing algorithms.
post Software 2.0 : How Neural Networks change the way we program.
book Deep Learning : Deep Learning go-to reference.
book The Elements of Statistical Learning : A classic on Machine Learning.
book Pattern Recognition and Machine Learning : A classic on Machine Learning.
book Machine Learning: a Probabilistic Perspective : A Machine Learning book with a probability view of algorithms.

course CS 329S: Machine Learning Systems Design : A full course on machine learning systems.
post Rules of Machine Learning : A guide with rules that should be considered when deploying models.
paper 150 successful Machine Learning models: 6 lessons learned at Booking.com : Interesting remarks on deploying Machine Learning models to production.
paper Machine Learning: The High Interest Credit Card of Technical Debt : Risk factors when deploying Machine Learning.

course Reinforcement Learning Specialization : Reinforcement Learning specialization offered by University of Alberta.
course David Silver’s course on RL : A good complement to the Reinforcement Learning specialization.
book Reinforcement Learning : The reference in Reinforcement Learning.

course Recommender Systems Specialization : A starting point to Recommender Systems.