Courses You MUST Take:
Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)
Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.
(https://work.caltech.edu/telecourse.html)
Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)
Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...)
Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)
Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)
From Open AI CEO Greg Brockman on Quora
Deep Learning Book (http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team )
It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg
2. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/) or Linear Algebra (MIT)
(http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...)
Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks
Neural Nets — Andrej Karpathy’s CS231N (Stanford)
http://cs231n.stanford.edu/
Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley)
https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/
Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/
Courses You MUST Take:
Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)
Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.
(https://work.caltech.edu/telecourse.html)
Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)
Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...)
Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)
Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)
From Open AI CEO Greg Brockman on Quora
Deep Learning Book (http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team )
It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg
2. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/) or Linear Algebra (MIT)
(http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...)
Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks
Neural Nets — Andrej Karpathy’s CS231N (Stanford)
http://cs231n.stanford.edu/
Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley)
https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/
Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/