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Python Machine Learning, 1st Edition
Purchase options and add-ons
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
Key Features
Book Description
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.
What you will learn
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.
- ISBN-109781783555130
- ISBN-13978-1783555130
- PublisherPackt Publishing
- Publication dateSeptember 1, 2015
- LanguageEnglish
- Dimensions7.5 x 1.03 x 9.25 inches
- Print length454 pages
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From the brand
Editorial Reviews
About the Author
Sebastian Raschka
Sebastian Raschka is a PhD student at Michigan State University, who develops new computational methods in the field of computational biology. He has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning. Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background. He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle. In his free time, he works on models for sports predictions, and if he is not in front of the computer, he enjoys playing sports.
Product details
- ASIN : 1783555130
- Publisher : Packt Publishing
- Publication date : September 1, 2015
- Language : English
- Print length : 454 pages
- ISBN-10 : 9781783555130
- ISBN-13 : 978-1783555130
- Item Weight : 1.71 pounds
- Dimensions : 7.5 x 1.03 x 9.25 inches
- Best Sellers Rank: #2,193,942 in Books (See Top 100 in Books)
- #660 in Data Modeling & Design (Books)
- #853 in Computer Neural Networks
- #934 in Data Processing
- Customer Reviews:
About the author

Sebastian Raschka, PhD is an LLM Research Engineer with over a decade of experience in artificial intelligence. His work bridges academia and industry, including roles as senior engineering staff at an AI company and a statistics professor.
As an independent researcher and industry expert, Sebastian collaborates with companies on AI solutions and serves on the Open Source Advisory Board at University of Wisconsin–Madison.
Sebastian specializes in LLMs and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.
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Reviews with images
Great Book.
Top reviews from the United States
There was a problem filtering reviews. Please reload the page.
- Reviewed in the United States on October 10, 2016Format: PaperbackVerified PurchaseIn 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!
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!
Images in this review
- Reviewed in the United States on November 2, 2015Format: PaperbackVerified PurchaseThis 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.
Top reviews from other countries
Y ZhaoReviewed in Canada on April 13, 20175.0 out of 5 stars great book for ML practitioners
Format: PaperbackVerified PurchaseI 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.
-
Oscar d.Reviewed in Mexico on June 2, 20191.0 out of 5 stars Cancelar la adquisición
Format: KindleVerified PurchaseNo me interesa adquirir el producto
Miler SilvaReviewed in Brazil on May 28, 20165.0 out of 5 stars Amazing
Format: PaperbackVerified PurchaseGreat intro to machine learning algorithms. Since the author focus mainly on algorithms (using Python's scientific libraries), the explanations may be non-mathematicians friendly.
-
stefano fedeleReviewed in Italy on August 25, 20185.0 out of 5 stars prima volta con machine learning
Format: PaperbackVerified PurchaseE' 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...
Daniel ManReviewed in the United Kingdom on March 17, 20165.0 out of 5 stars Great Introduction to Machine Learning with Scikit-Learn
Format: PaperbackVerified PurchaseGreat Introduction to Machine Learning with Scikit-Learn! Very well written, lots of examples. Very suitable for machine learning beginners with python experience!






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