Book Image

Python: Deeper Insights into Machine Learning

By : David Julian, Sebastian Raschka, John Hearty
Book Image

Python: Deeper Insights into Machine Learning

By: David Julian, Sebastian Raschka, John Hearty

Overview of this book

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. 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. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.
Table of Contents (6 chapters)
4
A. Biblography
5
Index

Chapter 4. Models – Learning from Information

So far in this book, we have examined a range of tasks and techniques. We introduced the basics of data types, structures, and properties, and we familiarized ourselves with some of the machine learning tools that are available.

In this chapter, we will look at three broad types of model:

  • Logical models
  • Tree models
  • Rule models

The next chapter will be devoted to another important type of model—the linear model. Much of the material in this chapter is theoretical, and its purpose is to introduce some of the mathematical and logical tools needed for machine learning tasks. I encourage you to work through these ideas and formulate them in ways that may help solve problems that we come across.

Logical models

Logical models divide the instance space, that is the set of all possible or allowable, instances, into segments. The goal is to ensure that the data in each segment is homogeneous with respect to a particular task. For example...