Artificial Intelligence Programming with Python

CHAPTER 3Machine Learning

“Before we work on artificial intelligence why don't we do something about natural stupidity?”

—Steve Polyak (American Neurologist)

CHAPTER OUTLINE

1. 3.1 Introduction
2. 3.2 Supervised Learning: Classifications
3. 3.3 Supervised Learning: Regressions
4. 3.4 Unsupervised Learning
5. 3.5 Semi-supervised Learning
6. 3.6 Reinforcement Learning
7. 3.7 Ensemble Learning
8. 3.8 AutoML
9. 3.9 PyCaret
10. 3.10 LazyPredict
11. 3.11 Summary
12. 3.12 Chapter Review Questions

3.1 Introduction

As you saw in Chapter 1, AI covers a broad area, and one of most important aspects of AI is machine learning.

Machine learning (ML) is basically a set of mathematical algorithms developed in the 1980s. Machine learning is an important subset of AI, and it is the science that aims to teach computers, or machines, to learn from data and to analyze data automatically, without human intervention. It includes a set of mathematical algorithms that can make a decision or, more accurately, predict the results for a given set of data. The term machine learning was coined by Arthur Samuel, an American pioneer in computer science and artificial intelligence, in 1959 when he was working at IBM. In 1997, a more modern definition of machine learning was provided by Tom Mitchell as “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Machine learning can be divided into these ...