Machine Learning. Theory and Practice

Chapter 0

Introduction

We constantly encounter various problems and try to solve them using timely technologies. Each of us knows that many everyday tasks are solved spontaneously and quickly, often without conscious effort. This is because, from birth, we learn to deal with such problems; therefore, our solutions are encoded in the neural cells of the brain. Solving complex problems, especially in science or engineering, requires deep reflection and possibly extensive research. But what should be done when it is necessary to answer...

24 min 5 Tests

Chapter 2

Python Basics

This section presents the basic principles of using the Python programming language to create machine-learning models. Python is a high-level programming language with extensive capabilities. It does not require compilation, yet at the same time it provides a certain level of efficiency comparable to machine-code-level programming languages. Python has established itself as the preferred tool for creating tools and applications in the field of machine learning. Thanks to its extensive collection of open and...

159 min 5 Tests

Chapter 3

Basic Mathematical Computations

This chapter discusses typical scientific computations using Python code. We will focus on how numerical data are represented mathematically, how they are structured or organized, stored, manipulated, processed, and computed in efficient ways. The subtleties of these operations in Python will be considered. All code presented in this textbook, and in this chapter in particular, can be found at https://sohoware.ru/SohoBook/. Our discussion begins with several basic linear-algebra operations on data structured as...

99 min 5 Tests

Chapter 4

Learning Models Based on Statistics and Probability

This chapter examines several aspects of probability theory and statistics that are relevant to machine-learning models, as well as methods for computing them using Python. All code presented in this textbook, and in this chapter in particular, can be found at https://sohoware.ru/SohoBook/. Creating a machine-learning model is usually aimed at prediction, classification, or identification based on available data and knowledge about those data. Predictions may be deterministic or probabilistic. Often we want to...

96 min 5 Tests

Chapter 5

Prediction Function and Universal Prediction Theory

From the standpoint of machine learning (ML), an important element in creating effective predictive models is the hypothesis that defines the prediction function. This function establishes the relationship between feature variables and learning parameters. Its choice must be justified. The main task of the prediction function is to predict any linear function in the feature space with high accuracy by using a specific set of learning parameters. It is important that the prediction function is assumed to be a...

59 min 5 Tests

Chapter 6

Perceptron and Support Vector Methods (SVM)

This chapter examines the fundamental aspects of object classification using machine-learning techniques, in particular the perceptron and support vector methods (SVM, Support Vector Machine). All code presented in this textbook, and in this chapter in particular, can be found at https://sohoware.ru/SohoBook/. In the study and application of computer science and artificial intelligence, a key element is understanding and processing different types of data, which may range from physical objects to abstract...

106 min 5 Tests