Python Machine Learning, 1st Edition

In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics. There are a couple of things that I really liked about this book. 1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ . 2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy. 3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it. Overall I would say that this book helped me and that I learnt a bunch of new things. If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience. - I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more. - Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off. I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem. Reasons why you shouldn't buy this book: Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case). I have also included some pictures. Great Book. Highly Recommend it!

I'm a senior undergraduate student in electrical and computer engineering, and decided to make use of machine learning for my senior design project. Having had some experience in python (but not much with matplotlib or scipy), I decided on this book because of all the other good reviews. I have since picked up a few other books related to machine learning, but none can even compare to this. It's stellar! In three weeks I have managed to give myself a comprehensive crash course in classification algorithms using Python which is enough to give me a rolling start on my design project. I am about half way through the book, and apart from very few minor errors (to be expected in a first edition book), I cannot find any faults in it. It's a great resource for someone who wants to learn about machine learning but doesn't know where to start. I'm going to keep an eye out for further books by Mr. Raschka, because his ability to clearly and concisely explain things is superb. Additionally, I enjoy the fact that the book attempts to give a solid foundation on the mathematics behind various machine learning algorithms, since that is enjoyable for someone like me, who always likes to understand what is happening beneath the surface. Update: Having finished the book now, I can definitely reaffirm my original position. This is one of the best technical books I have ever read. The last few chapters especially, image recognition with MLP networks and parallelizing networks with Theano and Keras are extremely interesting. I have taken these ideas and applied them in several of my own projects now. Also, as I'm planning on going to graduate school in the very near future, I'm thinking that machine learning and ANNs will likely be at the top of my list of areas to specialize in. The research that is going on in this field is huge, and this book manages to touch at the very base of neural networks, but enough to get your feet wet and show you where to go from there.

I have been an ML practitioner for years. The majority of my time has been spent on deducting formulas and work with stats models. I like this book as it provides some great tips for ML production in Python. Before reading the book, I did not know some of the utility functions, such as stratified k-fold, are already there in sklearn. Because I do not worry about the theory and the implementation, I quickly flew through the book in days and learned some interesting points. I would recommend this book to the software engineers/developers who want to start a career in data science. It may not be a good one for research community as at many points the discussion could be superficial. However, this makes sense as the depth is not the focus of the book:) One improvement I expect from the next version(if possible) is the color -- b/w makes the figures extremely hard to follow.

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Great intro to machine learning algorithms. Since the author focus mainly on algorithms (using Python's scientific libraries), the explanations may be non-mathematicians friendly.

E' stato il mio primo approccio al Machine Learning, avendo una base di matematica e statistica a livello universitario e di programmazione in Python per applicazioni scientifiche (Numpy, Pandas, Scipy, Matplotlib). L'ho trovato molto chiaro e molto bello. Credo sia utile anche per coloro che vogliano approfittare per imparare a lavorare in Python. Gli ultimi 2 capitoli riguardano il deep learning e sembra esser un po l'introduzione di un altro libro da studiare...

Great Introduction to Machine Learning with Scikit-Learn! Very well written, lots of examples. Very suitable for machine learning beginners with python experience!