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.

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