Machine learning is one of the biggest trends that the world of computing has seen. Machine learning systems have a profound and exciting ability to provide important insights on an amazing variety of applications, from ground-breaking and lifesaving medical research to discovering fundamental physical aspects of our universe; from providing us with better, cleaner food to web analytics and economic modeling. In fact, there is hardly any area of our lives that is not touched by this technology in some way. Everyone wants to get into the field of machine learning, and in order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.
Chapter 1, Thinking in Machine Learning, gets you started with the basics of machine learning, and as the title says, it will help you think in the machine learning paradigm. You will learn the design principles and various models involved in machine learning.
Chapter 2, Tools and Techniques, explains that Python comes equipped with a large library of packages for machine learning tasks. This chapter will give you a flavor of some huge libraries. It will cover packages such as NumPy, SciPy, Matplotlib, and Scilit-learn.
Chapter 3, Turning Data into Information, explains that raw data can be in many different formats and can be of varying quantity and quality. Sometimes, we are overwhelmed by data, and sometimes we struggle to get every last drop of information from our data. For data to become information, it requires some meaningful structure. In this chapter, we will introduce some broad topics such as big data, data properties, data sources, and data processing and analysis.
Chapter 4, Models – Learning from Information, takes you through the logical models—where we explore a logical language and create a hypothesis space mapping, tree models – where we will find that they can be applied to a wide range of tasks and are both descriptive and easy to interpret; and rule models – where we discuss both ordered rule list- and unordered rule set-based models.
Chapter 5, Linear Models, introduces one of the most widely used models that forms the foundation of many advanced nonlinear techniques, such as support vector machines and neural networks. In this chapter, we will study some of the most commonly used techniques in machine learning. We will create hypothesis representations for linear and logistic regression.
Chapter 6, Neural Networks, introduces the powerful machine learning algorithm of artificial neural networks. We will see how these networks are a simplified model of neurons in the brain.
Chapter 7, Features – How Algorithms See the World, goes through the different types of feature—the Quantitative, Ordinal, and Categorical features. We will also learn the Structured and Transforming features in detail.
Chapter 8, Learning with Ensembles, explains the reason behind the motivation for creating machine learning ensembles, which comes from clear intuitions and is grounded in a rich theoretical history. The types of machine learning ensemble that can be created are as diverse as the models themselves, and the main considerations revolve around three things: how we divide our data, how we select the models, and the methods we use to combine their results.
Chapter 9, Design Strategies and Case Studies, looks at some design strategies to ensure your machine learning applications perform optimally. We will learn model selection and parameter tuning techniques, and apply them to several case studies.
All you need is an inclination to learn machine learning and the Python V3 software, which you can download from https://www.python.org/downloads/.
This book is for data scientists, scientists, or just the curious. You will need to know some linear algebra and some Python. You will need to have some basic knowledge of machine learning concepts.
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: "NumPy uses a dtype object to describe various aspects of the data."
Any command-line input or output is written as follows:
import numpy as np import matplotlib.pyplot as plt x = np.arange(0., 5., 0.2) plt.plot(x, x**4, 'r', x, x*90, 'bs', x, x**3, 'g^') plt.show()
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: "Clicking the Next button moves you to the next screen."
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