Description: by Alexandros Zenonos
Starting fresh in data science can be challenging sometimes. There are plenty of courses out there that promise to teach you everything from how to code to what is Principal Component Analysis (PCA) or train a Deep Neural Network. Some of them might do. But in my opinion, there are some books that can greatly benefit any data scientist. Shortlisting all the books I think are likely to be useful will rather confuse the reader as to where to start from. Besides I am sure there are tons of resources to find lo
This is one of the first books I read to understand and learn about machine learning in general. In particular, I enjoyed the pages where it describes how a decision tree works; you know the building block of perhaps the more popular random forest approach. So, it teaches you to calculate entropy (the amount of information) recursively for each attribute/feature in your data. I encourage you to read that part as it is as straightforward as teaching it can get — check out page 52 onwards. The language is s
This book approaches machine learning from a statistical perspective, which I believe is essential to understand why machine learning algorithms actually work, what could maybe go wrong and understand the importance of having good data. The book spends a lot of pages on linear models but it is worth it as you will get slowly introduced to the concepts. But don’t think that this is just that. It covers everything including neural networks even if it doesn’t get into Deep Learning, which is a “recent” trend a