I spent most of the last several months on studying programming and machine learning. This is difficult in many ways:
- Previously I had only a limited experience with programming, and now I need it. So I decided to study Python, which is a great, popular and not difficult to learn. It took me 1-1,5 months to learn enough to start studying machine learning. I went through a great book "Automate the boring staff with Python", it was amazing. Then I read some useful articles, practiced on sites offering exercises and so on;
- I had to renew my knowledge of linear algebra, calculus and other mathematical stuff. This is really necessary for understanding of machine learning algorithms;
- Also I needed statistics and theory of probabilities. While I have already renewed my understanding of statistics when I was preparing for Lean Six Sigma exams, theory of probabilities was never my forte. So it required some efforts to study it;
- Of course analytic skills are also necessary.
And machine learning by itself is very challenging. There many areas in machine learning and they differ from each other. Of course, while programming you could use libraries for fast and easy use of many algorithms, but if you need to reach better accuracy and performance than average, you have to understand how these algorithms work, what is the math and logic behind them.
Starting machine learning is easy thanks to a variety of guides and learning materials. Understanding it is much more difficult. I hope I'll be able to do it.
Meanwhile I am in process of building my portfolio to show my skills and knowledge in this sphere.