---
product_id: 18474107
title: "Python Machine Learning, 1st Edition"
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# Python Machine Learning, 1st Edition

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## Description

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask and answer tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Review: Great Book. - 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!
Review: My new #1 Python ML book! - 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.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #2,356,545 in Books ( See Top 100 in Books ) #808 in Data Modeling & Design (Books) #1,010 in Computer Neural Networks #1,100 in Data Processing |
| Customer Reviews | 4.3 out of 5 stars 263 Reviews |

## Images

![Python Machine Learning, 1st Edition - Image 1](https://m.media-amazon.com/images/I/71lHrK97qfL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Great Book.
*by P***R on October 10, 2016*

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!

### ⭐⭐⭐⭐⭐ My new #1 Python ML book!
*by B***S on November 2, 2015*

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.

### ⭐⭐⭐⭐ Great scikit-learn (Python) reference. Publisher still needs work on their template/graphics
*by A***. on October 27, 2015*

Update 11/2/2015: Finally got the replacement from Amazon. This one is in perfect shape, i.e., no alignment issues at all. Woohoo! Just as promised. For other details, please see my 10/29/15 review below. Update 10/29/2015: After having a conversation with the author (see comments), I'm updating my review. Here are some updated remarks on my original comments: I've been pushed a new copy from Amazon and promised properly aligned pages. I took 2 stars off for the misaligned pages (I'm emailing a picture of these to the author), but I'm giving these stars back because - as per our conversation - my copies were outliers and hopefully the new copy I receive will be properly printed. The three copies the author has are printed properly and mine shouldn't have been different. I'm also adding a star back because the author explained how the publisher/graphics team didn't do their job right. In conclusion, I'm taking off just one star because the whole 'package' I received isn't how I expected to be, but this isn't a poor reflection on the author. Actually, it's quite the contrary, and I stand by my statement that his content, ideas, thoughts, code and plots do their job well. The breadth of information is immense, and the depth on each topic is enough that I would consider this a great scikit-learn reference. Original review 10/26/2015 (deprecated): I didn't want to give this a bad review, trust me. I believe the content is great, the author has done a good job, and he's clearly knowledgeable on the subject matter. The content seems strong, covers a wide-variety of topics, and is a great reference on the Python scikit-learn package. I'm not sure one would be able to learn machine learning from this book alone, primarily because it's actually a reference on scikit-learn, but also because the algorithms are not implemented from scratch and instead covered in concept (and implemented in sklearn with examples here and there). As a reference to sklearn, this book does its job well. My issue is with the publisher and the editors. EVERY page is printed misaligned, i.e., not printed parallel to the paper. More alarmingly, opposite pages are misaligned in OPPOSITE directions, giving the perception that the pages are misaligned more than they really are! It's horribly distracting while reading the content. I thought it was a one-off thing, but I have seen this in two copies so far. I took off 2 stars for this. The other issue is with the formatting of the book. Have a look at the contents, for example; there is no line-spacing before or after chapter titles. Going through the contents section feels like I'm reading strange prose with italics, bold and underlined text thrown in here and there. I have to pretty much actively parse the entire contents to find what I'm looking for, or give up in the contents and jump back to the index. It's an annoying, utterly unsatisfying experience and pushes me away from the book. I took a star off here. The final thing wrong with this book are the illustrations. The charts/plots themselves are okay; they seem to be implemented in matplotlib, our go-to Python plotting library. They do their job, but definitely lack a certain je ne sais quoi. I guess they could've been prettier (think Bokeh or ggplot plotting libraries), but like I said I'm neutral on this. My problem is with the conceptual illustrations. That is, when the author is explaining machine learning or data science concepts, he seems to rely on what looks like MS PowerPoint 2003 or older to build out his diagrams. These plots are so 2003 that I have a hard time gleaning the concepts they're trying to push across. I'm sure the editors could've easily spent some more time recreating these charts in a newer version of PowerPoint (maybe with 2010+ design principles), or even better, in an actual graphic-design platform. Imagine browsing webpages on a Palm Pilot after using an iPhone/Android device for the last decade or so. You're not going to like it, it's going to slow you down, and maybe it'll even impede learning; this is what the illustrations are like. Ugh - I really wish the author, publisher or editor hadn't used the default template on PowerPoint 2003. Puts a bad taste in my mouth every time I think about it. Minus one star for this.

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