tldr: the ML algorithms look hard reading the papers, while the code looks simpler and shorter, also you can get pretty decent results in a few lines of R/Pytho

tldr: the ML algorithms look hard reading the papers, while the code looks simpler and shorter, also you can get pretty decent results in a few lines of R/Python/Ruby so ML is not that complex. I disagree in so many ways: 1. complex algorithms are usually very short in practice (e.g. dijkstra's shortest path or edit distance are the firsts that come to mind) 2. ML is not just applying ML algorithms: you have to evaluate your results, experiment with features, visualize data, think about what you can exploit and discover patterns that can improve your models. 3. If you know the properties of the algorithms you are using then you can have some insights that might help you on improving your results drastically. It's very easy to apply the right algorithms with the wrong normalizations and still get decent results in some tests.