Repetition/drilling for understanding and examination of knowledge by self-testing are wholly absent.
Projects like "free code camp" can dodge this problem because the staff is working for free as an open source project, but it would take a lot more 5 minute segments to teach someone linear algebra, the theory behind any type of model, and the background information that is necessary to generate provably compelling insight than companies like Codeacademy seem interested in tackling.
1. Any non-trivial ML will require significant amount of computational power (RAM/CPU/GPU). Who will subsidize that for participants? It's not sufficient to have a laptop that can run a browser.
2. Notice the proliferation of data science bootcamps. Once the syllabus/infrastructure becomes streamlined (and cheap), I would expect there to be more online offerings.
That said, there might currently be a market need for a syllabus based on Kaggle or other freely available data sets, and free compute resources provided on cloud platforms.
Here are the resources I found useful:
Advices from Open AI, Facebook AI leaders
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)
Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/
From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora
In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.