In this lesson, we learned the basics of classification. After discovering the goals of classification, and loading and formatting data, we discovered two classification algorithms: K-Nearest Neighbors and support vector machines. We used custom classifiers based on these two methods to predict values. In the next lesson, we will use trees for predictive analysis.
Artificial Intelligence and Machine Learning Fundamentals
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Artificial Intelligence and Machine Learning Fundamentals
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Overview of this book
Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.
As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.
By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
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Current Title:
Artificial Intelligence and Machine Learning Fundamentals
Table of Contents (10 chapters)
Artificial Intelligence and Machine Learning Fundamentals
Preface
Free Chapter
Principles of Artificial Intelligence
AI with Search Techniques and Games
Regression
Classification
Using Trees for Predictive Analysis
Deep Learning with Neural Networks
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