I have an electrical engineering background and the recent developments of ML, Deep Learning & AI is very interesting and I hoped to self-learn this. I signed up for Udacity's Machine Learning Nano Degree program and found it to be at a much higher level than what I had hoped. They usually drop an algorithm and talk about using it to solve the problem, rather than getting into the rudiments of it. Could you please suggest some methods - books, videos and general techniques to master the theory as well as the practical aspects of ML?
Some good resources... probably many more.<p>deep learning book <a href="http://www.deeplearningbook.org/" rel="nofollow">http://www.deeplearningbook.org/</a> for theory.
Cs231 <a href="http://cs231n.github.io/" rel="nofollow">http://cs231n.github.io/</a>
<a href="http://yerevann.com/a-guide-to-deep-learning/" rel="nofollow">http://yerevann.com/a-guide-to-deep-learning/</a>
Andrew Ng's Coursera course simply titled "Machine Learning" is good - it addresses the mathematics of fundamental algorithms and concepts while giving practical examples and applications: <a href="https://www.coursera.org/learn/machine-learning" rel="nofollow">https://www.coursera.org/learn/machine-learning</a><p>Regarding books, there are many very high quality textbooks available (legitimately) for free online:<p>Introduction to Statistical Learning (James et al., 2014) <a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow">http://www-bcf.usc.edu/~gareth/ISL/</a><p>the above book shares some authors with the denser and more in-depth/advanced<p>The Elements of Statistical Learning (Hastie et al., 2009) <a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow">http://statweb.stanford.edu/~tibs/ElemStatLearn/</a><p>Information Theory: Inference & Learning Algorithms (MacKay, 2003) <a href="http://www.inference.phy.cam.ac.uk/itila/p0.html" rel="nofollow">http://www.inference.phy.cam.ac.uk/itila/p0.html</a><p>Bayesian Reasoning & Machine Learning (Barber, 2012) <a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online" rel="nofollow">http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...</a><p>Deep Learning (Goodfellowet al., 2016) <a href="http://www.deeplearningbook.org/" rel="nofollow">http://www.deeplearningbook.org/</a><p>Reinforcement Learning: An Introduction (Sutton & Barto, 1998) <a href="http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html" rel="nofollow">http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.ht...</a><p>^^ the above books are used on many graduate courses in machine learning and are varied in their approach and readability, but go deep into the fundamentals and theory of machine learning. Most contain primers on the relevant maths, too, so you can either use these to brush up on what you already know or as a starting point look for more relevant maths materials.<p>If you want more practical books/courses, more machine-learning focussed data science books can be helpful. For trying out what you've learned, Kaggle is great for providing data sets and problems.