Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (Nlp) Applications

I noticed this in my bookshelf last week, and apparently I had bought it and forgotten to even take a look. I started skimming and was surprised at the usefulness of this book. It is very good.Pros:It does an excellent job of explaining fundamentals and common workflows of NLP. I know this, because my work is primarily NLP. It covers fundamentals quite well, such as tokenization, word vextors, similarity, classification; topic modeling, etc. It also gets into topic modeling. Later, there is a whole chapter about explainability of NLP models, which I am excited to read. I adore NLP insights.Cons:Like cookbooks, most things are blueprints. That’s very useful if you like those kinds of boxy explanations. I personally prefer typical book format, but this works too. I just find it a little distracting.But the book is rare in that it really explains the fundamentals. Many books junp straight to ML, or are only ML. This is good for foundation. It is also really useful and practical. This is now in my top four favorite NLP books. The pros absolutely outweigh the cons. And the datasets seem wonderful.I’m still reading and learning from this. Really glad I noticed I forgot to read.

Connaître les méthodes de nlp

What distinguishes this book from others in the same area is that all recipes included lead to an actual insight at the end, authors don't forget the so what behind the code and the analysis. Recipes are combined with some theory background too to help build intuition about the key concepts, which is useful. I come from an R background, but found the Python code easy to follow (use of many familiar data frame concepts).

I really enjoyed reading this book as it comprehensively and clearly explains the fundamentals of NLP, while also putting it directly into practice.