Book Image

The Applied Artificial Intelligence Workshop

By : Anthony So, William So, Zsolt Nagy
Book Image

The Applied Artificial Intelligence Workshop

By: Anthony So, William So, Zsolt Nagy

Overview of this book

You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You will then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you’ll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you’ll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you’ll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models.

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Table of Contents (8 chapters)
Preface
About the Book
1
1. Introduction to Artificial Intelligence
1. Introduction to Artificial Intelligence
Introduction
Fields and Applications of AI
AI Tools and Learning Models
The Role of Python in AI
Python for Game AI
Heuristics
Pathfinding with the A* Algorithm
Introducing the A* Algorithm
Game AI with the Minmax Algorithm and Alpha-Beta Pruning
The Minmax Algorithm
DRYing Up the Minmax Algorithm – the NegaMax Algorithm
Summary
Free Chapter
2
2. An Introduction to Regression
2. An Introduction to Regression
Introduction
Linear Regression with One Variable
Linear Regression with Multiple Variables
Polynomial and Support Vector Regression
Support Vector Regression
Summary
3
3. An Introduction to Classification
3. An Introduction to Classification
Introduction
The Fundamentals of Classification
Data Preprocessing
The K-Nearest Neighbors Classifier
Classification with Support Vector Machines
Summary
4
4. An Introduction to Decision Trees
4. An Introduction to Decision Trees
Introduction
Decision Trees
The Confusion Matrix
Random Forest Classifier
Summary
5
5. Artificial Intelligence: Clustering
5. Artificial Intelligence: Clustering
Introduction
Defining the Clustering Problem
Clustering Approaches
The K-Means Algorithm
The Mean Shift Algorithm
Clustering Performance Evaluation
Summary
6
6. Neural Networks and Deep Learning
6. Neural Networks and Deep Learning
Introduction
Artificial Neurons
Neurons in TensorFlow
Neural Network Architecture
Activation Functions
Forward Propagation and the Loss Function
Backpropagation
Optimizers and the Learning Rate
Regularization
Deep Learning
Computer Vision and Image Classification
Recurrent Neural Networks (RNNs)
Summary
Appendix
1. Introduction to Artificial Intelligence
2. An Introduction to Regression
3. An Introduction to Classification
4. An Introduction to Decision Trees
5. Artificial Intelligence: Clustering
6. Neural Networks and Deep Learning

Introduction

In the previous chapter, you were introduced to regression models and learned how to fit a linear regression model with single or multiple variables, as well as with a higher-degree polynomial.

Unlike regression models, which focus on learning how to predict continuous numerical values (which can have an infinite number of values), classification, which will be introduced in this chapter, is all about splitting data into separate groups, also called classes.

For instance, a model can be trained to analyze emails and predict whether they are spam or not. In this case, the data is categorized into two possible groups (or classes). This type of classification is also called binary classification, which we will see a few examples of in this chapter. However, if there are more than two groups (or classes), you will be working on a multi-class classification (you will come across some examples of this in Chapter 4, An Introduction to Decision Trees).

But what is a...

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