Python Machine Learning: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics

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!

This is a fantastic book, even for a relative beginner to machine learning such as myself. The first thing that comes to mind after reading this book is that it was the perfect blend (for me at least) of theory and practice, as well as breadth and depth.Let’s face it, we know that machine learning isn’t an easy subject. You need theory…but you also need practice in the form of some serious coding before you really start understanding it. And this is one area where Sebastian’s book shines: it contains a plethora of really good code examples that are illuminating and well explained, and which cover a very wide range of different machine learning algorithms. And, speaking of code, as another reviewer has pointed out, another huge plus is that, in many places, Sebastian shows you how to gauge the performance of your code and make it more efficient.For me, the best measure of any book such as this is how many “ah ha!” moments I had while reading it. And I had more than a few while reading Sebastian’s book. One such “ah ha!” moment came while reading chapter 12 (and this also illustrates that nice blend of theory and practice I already mentioned above). In this particular chapter, he discusses training artificial neural networks for image recognition. At the heart of this approach is back propagation, which is pretty much THE bread and butter behind multilayered neural networks. He presents a detailed discussion of back propagation in two separate pieces: one that is intuitive and “top down”; the other a more mathematical, “bottoms up” approach that goes through the algorithm step by step, showing how the gradients are computed and the weights updated. His treatment of back propagation was one of the better explanations I’ve seen and really cleared things up for me.One last thing I must mention: at the time of release, this was the first machine learning book for Python (to my knowledge) that has an entire chapter devoted to Theano, which he uses to parallelize neural network training. For those who don’t know, Theano is a particularly nice (not to mention very powerful) Python library for doing machine learning, most especially if you can utilize the power of GPU computing. In addition, that particular chapter (13) also introduces the brand new Python library named Keras, which is built on top of Theano and is a really nice library for the rapid building and prototyping of neural networks (in the spirit of Torch). Being a brand new library, his treatment of Keras was necessarily brief, but it was a great starting point.In conclusion, I am very confident that if you do pick up this book, you won’t be at all disappointed. And be sure and grab the accompanying code for the book on his GitHub repository (just look for “python-machine-learning-book” on github.com/rasbt.) His code is top notch and I’ve yet to encounter any problems with it.