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I am learning data analytics and I wonder if there are some good references and tutorials about machine learning, data analytics and data mining?

What I'm searching for is an understandable reference/tutorial, which isn't very technical and isn't very basic either, in other words the material begins with the basic steps towards advanced steps.

Thank you.

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  • $\begingroup$ The main problem is that there are so many, and there is no clearly standard way to teach ML (probably because it's too recent) so while there are many good courses they tend to emphasize different goals/methods. $\endgroup$ – Erwan Jan 25 at 21:31
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    $\begingroup$ There are a good few questions here asking for recommended books like this one. This book is also often recommended. $\endgroup$ – Erwan Jan 25 at 21:38
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    $\begingroup$ See if Andrew Ng's Machine Learning course is still on Coursera. Andrew Ng explains concepts well and tries to avoid heavy mathematics whenever possible. The ML course is not to be confused with his Deep Learning course... On Coursera, you can audit without paying... Also, I agree with @Erwan that, at some point, you will have to understand the book by James et. al. $\endgroup$ – stans Jan 26 at 6:29
  • $\begingroup$ @Erwan Brilliant, thank you. $\endgroup$ – plpm Jan 26 at 23:11
  • $\begingroup$ @stans Thank you. $\endgroup$ – plpm Jan 26 at 23:13
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I recommend "An Introduction to Statistical Learning" (ISL) by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani. The book is available online.

The book covers a number of topics in ML and for each topic there is a "lab" in R (code is also available for Python, in fact there are several pages on Github covering the labs).

ISL is not too technical but gives a sound introduction. In case you want to advance on some topics, you can switch to the advanced book "Elements of Statistical Learning" (ELS).

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    $\begingroup$ Thank you very much, they are amazing books. I checked the first one and it seems to be what I am looking for. I had read some parts of ESL before. Hastie and Tibshirani are very good in explaining. I consider the combination of all your suggestions as a very helpful package. $\endgroup$ – plpm Jan 26 at 23:05

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