Machine Learning, sources of information

My problem

I want to learn Machine Learning concepts, understand how to apply on real cases.

There are so many sources of information, some of them with good/bad quality, some others very complex.

The questions are simple:

  • when is useful to use machine learning solution?
  • what is a neuronal net?
  • what are the steps to build a machine learning solution?
  • what are the type of algorithms you can use for specific problems?

The solution I found

  1. I’m doing some Skillsoft courses I have available in the company platform. These are covering the basis and now I’m jumping on specific topics. They are nice as they include examples in Python with scikit.
  2. Review these notes from @TessFerrandez: Notes from Coursera Deep Learning courses: https://www.slideshare.net/TessFerrandez/notes-from-coursera-deep-learning-courses-by-andrew-ng
  3. Watch the video from @TessFerrandez: Machine Learning for Developers . The video explains how the process of building a machine learning solution is, explained in plain English and with very nice examples easy to remind. The video helped me to link a lot of technical ideas explained in the courses with a natural flow.