Chapter 1, The Realm of Supervised Learning, covers various supervised-learning techniques for regression. We will learn how to analyze bike-sharing patterns and predict housing prices.

Chapter 2, Constructing a Classifier, covers various supervised-learning techniques for data classification. We will learn how to estimate the income brackets and evaluate a car based on its characteristics.

Chapter 3, Predictive Modeling, discusses predictive-modeling techniques using Support Vector Machines. We will learn how to apply these techniques to predict events occurring in buildings and traffic on the roads near sports stadiums.

Chapter 4, Clustering with Unsupervised Learning, explains unsupervised learning algorithms, including k-means and Mean Shift clustering. We will learn how to apply these algorithms to stock market data and customer segmentation.

Chapter 5, Building Recommendation Engines, teaches you about the algorithms that we use to build recommendation engines. We will learn how to apply these algorithms to collaborative filtering and movie recommendations.

Chapter 6, Analyzing Text Data, explains the techniques that we use to analyze text data, including tokenization, stemming, bag-of-words, and so on. We will learn how to use these techniques to perform sentiment analysis and topic modeling.

Chapter 7, Speech Recognition, covers the algorithms that we use to analyze speech data. We will learn how to build speech-recognition systems.

Chapter 8, Dissecting Time Series and Sequential Data, explains the techniques that we use to analyze time series and sequential data including Hidden Markov Models and Conditional Random Fields. We will learn how to apply these techniques to text sequence analysis and stock market predictions.

Chapter 9, Image Content Analysis, covers the algorithms that we use for image content analysis and object recognition. We will learn how to extract image features and build object-recognition systems.

Chapter 10, Biometric Face Recognition, explains the techniques that we use to detect and recognize faces in images and videos. We will learn about dimensionality reduction algorithms and build a face recognizer.

Chapter 11, Deep Neural Networks, covers the algorithms that we use to build deep neural networks. We will learn how to build an optical character recognition system using neural networks.

Chapter 12, Visualizing Data, explains the techniques that we use to visualize various types of data in machine learning. We will learn how to construct different types of graphs, charts, and plots.

There is a lot of debate going on between Python 2.x and Python 3.x. While we believe that the world is moving forward with better versions coming out, a lot of developers still enjoy using Python 2.x. A lot of operating systems have Python 2.x built into them. This book is focused on machine learning in Python as opposed to Python itself. It also helps in maintaining compatibility with libraries that haven't been ported to Python 3.x. Hence the code in the book is oriented towards Python 2.x. In that spirit, we have tried to keep all the code as agnostic as possible to the Python versions. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.

This book is for Python programmers who are looking to use machine learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming will certainly be useful to play around with the code.

In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).

To give clear instructions on how to complete a recipe, we use these sections as follows:

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Here, we allocated 25% of the data for testing, as specified by the test_size parameter."

A block of code is set as follows:

import numpy as np
import matplotlib.pyplot as plt

import utilities 

# Load input data
input_file = 'data_multivar.txt'
X, y = utilities.load_data(input_file)

Any command-line input or output is written as follows:

$ python object_recognizer.py --input-image imagefile.jpg --model-file erf.pkl --codebook-file codebook.pkl

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "If you change the explode array to (0, 0.2, 0, 0, 0), then it will highlight the Strawberry section."

Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.

To send us general feedback, simply e-mail <[email protected]>, and mention the book's title in the subject of your message.

If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.

Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.