I am teaching myself (or at least attempting to :-/) the practice of machine learning, but have found many resources out there to require much more advanced knowledge in other subjects (stats, LA, etc.) I have attempted several resources such as "Learning from Data" course with Yaser, Andrew NG Coursera ML, and several others. They do offer some great material but for somebody that is a complete novice they don't really offer much explanation to any of their exercises and how they came up with the solutions (in the homeworks for example, or problems within the text book).
I am looking and hoping for something out there that offers step by step instructions as to what is going on within a problem and detailed explanations to how they came up with answers to different exercises and problems (not just the answer). Also, not really looking for something that just has a forum to get answers that has several people just posting blurps of the problem in an unclear manner but something much more structed. For example a resource that basically says:
Problem 1 - <The question or problem to be solved here> Step 1)... then goes through step by step and very detailed instructions on how to come up with the solution or figure it out and perhaps even offers the code so that you may reverse engineer. This process of teaching I believe is best, because it helps in allowing to reverse engineer the problem and therefore understand it and apply the lesson to future problems. (at least this has shown to help me understand).
Also, if you know of any resources that instruct in this fashion while teaching how to to implement it in Python code, that would be greatly appreciated as well.
Thanks!
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