Machine Learning using Python

Language is clear. Concepts are explained with examples.

This book is one of the best for learning Python for Machine learning for beginners, especially those who do not come from coding background. After going through several courses on udacity, datacamp, yet failing screening Python coding rounds for Data Science positions, I realised that the subject matter coverage in this book is the most systematic and comprehensive.

I have been dedicatedly following this book for a while now and I would like to give you my honest and balanced feedback.This book alone will not help you scale up in ML. You will need chatGPT and I will clarify later why. However, coupled with ChatGPT, this book has everything you will ever need to know ML.Now coming back to why would you need chatGPT. This book will coast along through the topics nicely until it abruptly throws an ugly looking formula at you stating "XYZ is calculated as this ....". For example if you look at the snapshot, the book says that "the standard error of estimate is inflated in case of multi collinearity". When I read this for the first time, my immediate reaction was, "whoa. where did this suddenly come from? And most importantly, how is it inflated?". As you see, there wouldn't be any start and end to this text. If you only refer this textbook without any external help, you will probably give up understanding the content in all likelihood.This is precisely the reason I have been able to coast through this book now while I purchased this back in 2019 and gave up multiple times as chatGPT was not prevalent or probably non existant. Having said that, this book has everything explained at the most granular level possible. That means that if you are able to couple the content of this book with chatGPT, you would understand the ML concepts really well.To be fair, statistics for me is like an infinite well. Irrespective of how well you might think you've understood, there might be a new surprise awaiting you round the corner. I am sure the authors must have assumed this and tried to ensure to cover as much as possible but also leaving some work for the readers to do themselves.So my recommendation would be to please go ahead and buy this but be ready to put in the extra efforts to ask the right prompts to chatGPT and if possible, even document them alongside the text in this book.

I have just started reading it and read only 3 chapters of it, but can't resist myself from writing the review. I have been searching for the right book to start with the data science along with the online course and this book fits right in. The examples, way of writing the book are just excellent. Although this book is not comprehensive ( i believe no book on ML is) but it starts with the basic and clear concepts very well.Kudos to the authors.

Book has a wiley-india link where you can download codes and datasets used in the examples throughout the book.Running those codes in your own jupyter notebook gives good sense of what is happening(can add these in your github). Probability & stats part is not explained well, has to refer some other book for that.

Overall good book, I read first four chapters, basics were taught right.May be this book for basics in brief. Would have loved more, if the book had covered the more in life scenarios, hyper paramater tuning and optimization , with Important metrics to consider for real life problems.

Reviewing a book is always a very subjective matter. I already work on data sc. This book would give help me get quick tips. Like all, even this book is not comprehensive, but it addresses the topics at the basic level. I am very happy with this book, especially the examples used in this.

I received the book after 15 days via India Post. I Had to travel a 10-minute distance to pick it up as it wasn't delivered at home. The quality of the book is good but with some foldings in the paper. Haven't read it yet, I will update the review after reading a few chapters.