Top 5 Best Books for Machine Learning with Python

Machine learning provides computers with the ability to learn without being explicitly programmed. It is a subfield of computer science and a type of artificial intelligence (AI). The most popular language for machine learning is Python Programming Language. Because Python is an accessible, powerful and flexible language for machine learning. 

Python Machine Learning, 1st Edition
Author: Sebastian Raschka
Published at: 01/09/2015
ISBN: 1783555130

If you want to find out the question how to use Python to start and answering critical questions of your data, then this book Python Machine Learning is exactly for you. If you want to get started from scratch or want to extend your data science knowledge, then this is an essential and valuable resource.

What You Will Learn

  • You will learn how access to the world of machine learning and demonstrates why Python is one of the world leading data science languages.
  • This book helps you to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems.
  • This book 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 find out how to unlock trends and patterns in business-critical data with some of the most popular and important techniques for building sophisticated algorithms and statistical models.
  • You will learn how to break down complex ideas into simple and actionable results.
  • You'll learn how statistical and mathematical concepts relate to training artificial neural networks, probability theory, deep learning and much more.

Learning scikit-learn: Machine Learning in Python
Author: Raúl Garreta,Guillermo Moncecchi
Published at: 25/11/2013
ISBN: 1783281936

Learning scikit-learn: Machine Learning in Python 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.

What You Will Learn

  • You will learn how to incorporate machine learning in your applications.
  • The book also combines an introduction to some of the main concepts and methods in machine learning with practical hands-on example.
  • You will learn how to evaluate your results and apply advanced techniques for preprocessing data and selecting the best set of features and the best methods for each problem.
  • You will learn how to set up scikit-learn inside your Python environment.
  • This book also includes how to classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines.
  • The book using Decision Trees to explain the main causes of certain phenomenon such as the Titanic passengers survival.
  • You will learn how to predict house prices using regression techniques.
  • You will learn how to select the best parameters for your models using model selection.
  • You will also learn how to improve the way for building your models using parallelization techniques.

Machine Learning in Python: Essential Techniques for Predictive Analysis
Author: Michael Bowles
Published at: 27/04/2015
ISBN: 1118961749

Machine learning doesn't have to be complex and highly specialized. This book shows you how Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested without requiring an extensive background in math or statistics.

What You Will Learn

  • Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply those using Python.
  • This book has ability to provide you full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code.
  • You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. 
  • You will learn how to predict outcomes using linear and ensemble algorithm families.
  • You will learn how to build predictive models that solve a range of simple and complex problems.
  • The book covering how to apply core machine learning algorithms using Python.
  • Using sample code directly to build custom solutions.

Programming Collective Intelligence: Building Smart Web 2.0 Applications
Author: Toby Segaran
Published at: 26/08/2007
ISBN: 0596529325

Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general--all from information that you and others collect every day. 

What You Will Learn

  • You will learn how to collaborate filtering techniques that enable online retailers to recommend products or media.
  • Methods of clustering to detect groups of similar items in a large dataset.
  • Learning search engine features--crawlers, indexers, query engines, and the PageRank algorithm.
  • Using decision trees is not only to make predictions, but also model the way decisions are made
  • Predicting numerical values rather than classifications to build price models
  • Support vector machines to match people in online dating sites
  • You will learn non-negative matrix factorization to find the independent features in a data set.
  • You will learn how to evolving intelligence for problem solving--how a computer develops its skill by improving its own code the more it plays a game.

Building Machine Learning Systems with Python
Author: Willi Richert,Luis Pedro Coelho
Published at: 26/07/2013
Featuring a wealth of real-world examples, this book provides you with an accessible route into Python machine learning. You will learn the Iris dataset and find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity.

What You Will Learn

  • Learn how to create machine learning algorithms using the flexibility of Python.
  • Get to grips with scikit-learn and other Python scientific libraries that support machine learning projects.
  • You will learn how to employ computer vision using mahouts for image processing that will help you uncover patterns and trends in your data.
  • You will learn topic modelling and build a topic model for Wikipedia.
  • Analyze Twitter data using sentiment analysis.
  • Get to grips with classification and regression with real-world examples.

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