I am a beginner in ML, though I have completed Andrew Ng's Machine Learning course on Coursera and am soon to complete his Deep Learning specialization, also on Coursera. I'm looking for recommendations for things to study after that.

They need to be on the easier side since I am still learning. Also in addition to resources on ML or Deep Learning, I'm also interested in any resources on the underlying math. I have a book in probability and another book in calculus, though both are fairly dense. Anything lighter might be a plus.

Are there any resources you would recommend? I'm particularly interested in other courses on Coursera since that's what my company currently sponsors, though I'm interested in whatever suggestions you have to offer.

  • 3
    $\begingroup$ To understand the underlying math you really need a book like ESL, PRML, MLAPA, or IML. For statistics I recommend All of Statistics. Here are some online resources. $\endgroup$
    – Emre
    Feb 15 '18 at 1:00
  • $\begingroup$ Hastie and Efron's book "Compute age Statistical Inference" is a good addition to Emre's list. $\endgroup$ Mar 2 '18 at 15:21
  • $\begingroup$ Try fast.ai. They have both Deep Learning and shallow ML courses. And they are awesome! $\endgroup$ Mar 10 '18 at 8:29
  • $\begingroup$ Thanks @VincenzoLavorini, I will check it out. I see the deep courses. Where are the shallow ones? $\endgroup$
    – Stephen
    Mar 13 '18 at 17:07
  • $\begingroup$ I'm sorry, I remember wrong. It seems they never made those videos ("Intro to Machine Learning") public. So they are still available only to members of fast.ai community $\endgroup$ Mar 14 '18 at 8:59

If you're looking for an introduction to mathematics for data science, take a look at the Coursera course Data Science Math Skills.

  • $\begingroup$ I saw that but it doesn't look like it lives up to the name. You'll need more math than is contained in that short 4 week course (where the first two weeks are super basic stuff). $\endgroup$
    – Stephen
    Mar 14 '18 at 1:02

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