Clustering | Artificial Intelligence and Machine Learning Fundamentals

Book Overview & Buying
Table Of Contents
Feedback & Rating

Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

4.3 (110)

Buy this Book

Artificial Intelligence and Machine Learning Fundamentals

4.3 (110)

By: Nagy

Buy this Book

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!

Preface

About the Book

Free Chapter

Principles of Artificial Intelligence

Introduction

Fields and Applications of Artificial Intelligence

AI Tools and Learning Models

The Role of Python in Artificial Intelligence

Python for Game AI

Summary

AI with Search Techniques and Games

Introduction

Heuristics

Pathfinding with the A* Algorithm

Game AI with the Minmax Algorithm and Alpha-Beta Pruning

Summary

Regression

Introduction

Linear Regression with One Variable

Linear Regression with Multiple Variables

Polynomial and Support Vector Regression

Summary

Classification

Introduction

The Fundamentals of Classification

Classification with Support Vector Machines

Summary

Using Trees for Predictive Analysis

Introduction to Decision Trees

Random Forest Classifier

Summary

Clustering

Introduction to Clustering

The k-means Algorithm

Mean Shift Algorithm

Summary

Deep Learning with Neural Networks

Introduction

TensorFlow for Python

Introduction to Neural Networks

Deep Learning

Summary

Appendix

Chapter 1: Principles of AI

Chapter 2: AI with Search Techniques and Games

Chapter 4: Classification

Chapter 5: Using Trees for Predictive Analysis

Chapter 6: Clustering

Chapter 7: Deep Learning with Neural Networks

Customer Reviews

4.3 (110)

5 star

60%

4 star

21.8%

3 star

11.8%

2 star

1.8%

1 star

4.5%

The k-means Algorithm

The k-means algorithm is a flat clustering algorithm. It works as follows:

Set the value of K.
Choose K data points from the dataset that are initial centers of the individual clusters.
Calculate the distance of each data point to the chosen center points, and group each point in the cluster whose initial center is the closest to the data point.
Once all of the points are in one of the K clusters, calculate the center point of each cluster. This center point does not have to be an existing data point in the dataset; it is just an average.
Repeat this process of assigning each data point into the cluster that has a center closest to the data point. Repetition continues until the center points no longer move.

To make sure that the k-means algorithm terminates, we need the following:

A maximum level of tolerance when we exit in case the centroids move less than the tolerance value
A maximum number of repetitions of shifting...

Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

Artificial Intelligence and Machine Learning Fundamentals

By: Nagy

Overview of this book

The k-means Algorithm

Confirmation

Buy this book with your credits?

Submit Your Feedback

Create a Free Account To Continue Reading

Sign in to activate your 7-day free access