Learning Machine Learning: A beginner's journey
I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on.<p>I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way you can progress beyond lecture 2 (out of 20) without a solid probability background. A solid background in probability/statistics probably means a good first course in Probability or maybe the first five chapters of "Statistical Inference" by Cassias and Berger. Similarly, for SVM, you need a solid background in Linear Algebra and so on. You probably also need a background Linear Optimization. Here are the recommendations by Prof. Michael Jordan <a href="https://news.ycombinator.com/item?id=1055389" rel="nofollow">https://news.ycombinator.com/item?id=1055389</a><p>Not a lot of people want to dive in this much. They have got things to do and who cares about proofs anyway. The thinking goes like "Most of the mathematics is abstracted away by libraries like scikit-learn. Let's get shit done.". Well, I think a lot of competitive advantage of Google/Facebook in ML is because they have staffed their engineering with people who have studied these things for years (by PhD). Compare that to flipkart's recommendations.<p>However, I don't think this problem is unique to ML/Data Science. It is equally bad in "Distributed systems". Let's use Docker, that's the future!