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 scikit-learn: 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 scikit-learn: 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.
The book adopts a tutorial-based approach to introduce the user to Scikit-learn.
Who this book is 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.
Comments about oreilly Learning scikit-learn: Machine Learning in Python:
I am a software developer and father of 2 small boys. Add these together and I don't generally have a lot of time for reading and because of this that I tend to love Packt's [Instant] series. These short introductions give me an idea if I want to invest more of my time on a subject. I was already passingly acquainted with scikit-learn so this subject wasn't entirely new to me but, in this case, I can see that it might be a little harder for those coming completely blind to the subject. One of the great things about the book is the inclusion of code in the form of IPython notebooks making it fairly easy to get started tweaking and testing. It is well written and fairly easy to follow. Well, easy to follow if you already have a grasp on the maths and debugging of Python programmes. More in depth explanations would of course be great but this is an [Instant] book and you can't really expect and in depth coverage from it you just get your feet wet. I would have given the book 4 stars but unfortunately not all the code can be interpreted as is and that might be frustrating to some.
Bottom Line Yes, I would recommend this to a friend