 Название: An Introduction to Artificial Intelligence and Machine Learning I: By day-to-day examples
Автор: Manikandan Paneerselvam
Издательство: Notion Press
Год: 2023
Страниц: 390
Язык: английский
Формат: epub
Размер: 14.1 MB
How does our brain work in our routine life? The same way we design artificial intelligence in machines. Instead of complex straightforward theory, this book explains all logic and algorithms with the help of day-to-day examples. The language is straightforward. Besides, the examples are straightforward. We adequately cover all functions of the intelligent agent and machine learning models. This book is a sweet friend for newcomers to the AI field (this includes academic students and working professionals.). This book additionally includes statistical models. The overall intention of this book is to spread the knowledge to all kinds of readers preparing themselves to secure a visa for the upcoming AI- driven earth.
Few of us feel the algorithms and logic are complex to read if the phrase explains the theory. This book is changing the knowledge pattern. Most of the time, we plan to cover examples first before theory. Sometimes, we must keep the fundamental theory at the top. Next, however, we will cover the extensive examples at the bottom. Besides, we include all the predominant topics. We will cover all advanced deep-learning concepts in the three volumes. In this first volume, we address three crucial vital subjects.
• Artificial Intelligence
• Statistical Methods
• Machine Learning
We cover adequate knowledge of statistical methods. It covers the various lines of business. If someone is interested in statistics, our chapters are sufficient to start. Nevertheless, the intention of covering the statistical method is different, and these statistical methods should cover machine learning concepts.
We will publish volume II and volume III of this series soon.
Contents:
Preface
Chapter 1. Introduction to Artificial Intelligence and Machine Learning
Part 1. Artificial Intelligence
Part 2. Statistical Methods
Part. 3 Machine Learning
Chapter 15. Machine Learning: Introduction
Chapter 16. Machine Learning: Data Workflow and Data Mining
Chapter 17. Machine Learning: Linear Regression Models
Chapter 18. Machine Learning: Classification (Linear and Logistic classification)
Chapter 19. Machine Learning: Decision Tree
Chapter 20. Machine Learning: Instance-based Learning Algorithms
Chapter 21. Machine Learning: Support Vector Machine
Chapter 22. Machine Learning: Bayesian Learning
Chapter 23. Machine Learning: Ensemble Learning
Chapter 24. Machine Learning: Unsupervised Learning
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