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

Clustering Performance Evaluation

Unlike supervised learning, where we always have the labels to evaluate our predictions with, unsupervised learning is a bit more complex as we do not usually have labels. In order to evaluate a clustering model, two approaches can be taken depending on whether the label data is available or not:

  • The first approach is the extrinsic method, which requires the existence of label data. This means that in absence of label data, human intervention is required in order to label the data or at least a subset of it.
  • The other approach is the intrinsic approach. In general, the extrinsic approach tries to assign a score to clustering, given the label data, whereas the intrinsic approach evaluates clustering by examining how well the clusters are separated and how compact they are.

    Note

    We will skip the mathematical explanations as they are quite complicated.

    You can find more mathematical details on the sklearn website at this URL: https://scikit...

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