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Learning scikit-learn: Machine Learning in Python
Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level. Overview In Detail Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of “big data�, huge amounts of information is being generated. This makes machine learning an unavoidable source of new data-based approximations for problem solving. With Learning Machine Learning in Python, you will learn to incorporate machine learning in your applications. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python. The book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques. You will learn to evaluate your results and apply advanced techniques for preprocessing data. You will also be able to select the best set of features and the best methods for each problem. With Learning Machine Learning in Python you will learn how to use the Python programming language and the scikit-learn library to build applications that learn from experience, applying the main concepts and techniques of machine learning. What you will learn from this book Approach The book adopts a tutorial-based approach to introduce the user to Scikit-learn. Who this book is written for If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.
118 pages, Paperback
First published November 25, 2013
8 people are currently reading
49 people want to read
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Displaying 1 - 8 of 8 reviews
December 7, 2014
When you browse Amazon’s catalog and read “…the book starts with a brief introduction to the core concepts of machine learning. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques. You will learn to evaluate results and apply advanced techniques.� you expect the book to deliver. It does not, which makes it a big disappointment.
What’s wrong with the title? Well, the book does not teach the scikit-learn package. It does show a very quick overview of some of its features.
What about the description? Well, the book does not introduce you to the concepts of machine learning. On the contrary, unless you have a decent background in ML, you will get lost. The authors don’t say a word about matplotlib and numpy either but believe me, if you can’t get through some code with confidence, you will end up looking at snippets of 10-15 lines without understanding what’s happening, Stack Overflow won’t save you.
The book itself is not that bad. I enjoyed the pages describing decision trees. I think this book, rather than being sold, should be used in the official scikit-learn webpage, as an overview.
Not worth the price. Not at all.
As usual, you can find more reviews on my personal blog: . Feel free to pass by and share your thoughts!
What’s wrong with the title? Well, the book does not teach the scikit-learn package. It does show a very quick overview of some of its features.
What about the description? Well, the book does not introduce you to the concepts of machine learning. On the contrary, unless you have a decent background in ML, you will get lost. The authors don’t say a word about matplotlib and numpy either but believe me, if you can’t get through some code with confidence, you will end up looking at snippets of 10-15 lines without understanding what’s happening, Stack Overflow won’t save you.
The book itself is not that bad. I enjoyed the pages describing decision trees. I think this book, rather than being sold, should be used in the official scikit-learn webpage, as an overview.
Not worth the price. Not at all.
As usual, you can find more reviews on my personal blog: . Feel free to pass by and share your thoughts!
February 11, 2014
Short Answer
I'll recommend the book to people who can debug python codes by themselves and have some basic machine learning knowledge.
This book gives a short and brief introduction for scikit-learn. I did get some ideas about how to use scikit-learn to do some basic machine learning things. I regard this book as a more detailed document. It might be better if it can provide more mathematics intuition.
Pros
Quickly understand how scikit-learn works if you have already known some python and machine learning
Awesome IPython Notebook
Cons:
Some codes cannot be compiled.
Some algorithms haven't been described clearly.
Some libraries such like Pandas hasn't been described clearly.
Lack of Math intuition.
I'll recommend the book to people who can debug python codes by themselves and have some basic machine learning knowledge.
This book gives a short and brief introduction for scikit-learn. I did get some ideas about how to use scikit-learn to do some basic machine learning things. I regard this book as a more detailed document. It might be better if it can provide more mathematics intuition.
Pros
Quickly understand how scikit-learn works if you have already known some python and machine learning
Awesome IPython Notebook
Cons:
Some codes cannot be compiled.
Some algorithms haven't been described clearly.
Some libraries such like Pandas hasn't been described clearly.
Lack of Math intuition.
January 3, 2024
It is not irregular for books about fast paced languages or libraries to become outdated quickly, but this book was both outdated and full of errata. There is a Github repo of iPython Notebooks to help, but I discovered that the further I went in the book the less accurate even these became to the point where I had to give up following along to the examples because they were so bad.
Instead of reading this book, read the manuals and documentation on scikit learn's website - they are more thorough, up to date, and maybe a little less dry.
Instead of reading this book, read the manuals and documentation on scikit learn's website - they are more thorough, up to date, and maybe a little less dry.
June 21, 2020
Este libro de por si no es suficiente, no funciona como libro introductorio. Necesitas de un libro previo para poder saber cómo manejar la implementación
January 11, 2020
Great for beginners
This was my very first machine learning book I read and gave me a very nice and practical overview of everything. Code might be outdated, but that shouldn’t affect your reading.
This was my very first machine learning book I read and gave me a very nice and practical overview of everything. Code might be outdated, but that shouldn’t affect your reading.
December 21, 2020
Livro curto e cumpre o seu papel: mostrar as principais bibliotecas do Scikit-learn com exemplos práticos.
May 26, 2014
a very brief introduction into scikit. Perfectly OK for me because I am new to scikit myself. Codes are easy to follow. But for a more serious scikit docu, take the Building machine learning systems with Python.
June 6, 2015
Not as good as the other book from Packt on Machine Learning in Python ( for my full review)
Displaying 1 - 8 of 8 reviews
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