Book Image

Artificial Intelligence and Machine Learning Fundamentals

By : Zsolt Nagy
Book Image

Artificial Intelligence and Machine Learning Fundamentals

By: Zsolt Nagy

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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Table of Contents (10 chapters)
Artificial Intelligence and Machine Learning Fundamentals
Artificial Intelligence and Machine Learning Fundamentals
Preface
Preface
Free Chapter
1
Principles of Artificial Intelligence
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
2
AI with Search Techniques and Games
AI with Search Techniques and Games
Introduction
Heuristics
Pathfinding with the A* Algorithm
Game AI with the Minmax Algorithm and Alpha-Beta Pruning
Summary
3
Regression
Regression
Introduction
Linear Regression with One Variable
Linear Regression with Multiple Variables
Polynomial and Support Vector Regression
Summary
4
Classification
Classification
Introduction
The Fundamentals of Classification
Classification with Support Vector Machines
Summary
5
Using Trees for Predictive Analysis
Using Trees for Predictive Analysis
Introduction to Decision Trees
Random Forest Classifier
Summary
6
Clustering
Clustering
Introduction to Clustering
The k-means Algorithm
Mean Shift Algorithm
Summary
7
Deep Learning with Neural Networks
Deep Learning with Neural Networks
Introduction
TensorFlow for Python
Introduction to Neural Networks
Deep Learning
Summary
8
Appendix A
Appendix A
Lesson 1: Principles of AI
Lesson 2: AI with Search Techniques and Games
Lesson 4: Classification
Lesson 5: Using Trees for Predictive Analysis
Lesson 6: Clustering
Lesson 7: Deep Learning with Neural Networks

TensorFlow for Python

TensorFlow is one of the most important machine learning and open source libraries maintained by Google. The TensorFlow API is available in many languages, including Python, JavaScript, Java, and C. As TensorFlow supports supervised learning, we will use TensorFlow for building a graph model, and then use this model for prediction.

TensorFlow works with tensors. Some examples for tensors are:

  • Scalar values such as a floating point number.

  • A vector of arbitrary length.

  • A regular matrix, containing p times q values, where p and q are finite integers.

  • A p x q x r generalized matrix-like structure, where p, q, r are finite integers. Imagine this construct as a rectangular object in three dimensional space with sides p, q, and r. The numbers in this data structure can be visualized in three dimensions.

  • Observing the above four data structures, more complex, n-dimensional data structures can also be valid examples for tensors.

We will stick to scalar, vector, and regular matrix...

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