Book description
With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities
Key Features
 Learn about AI and ML algorithms from the perspective of a seasoned data scientist
 Get practical experience in ML algorithms, such as regression, tree algorithms, clustering, and more
 Design neural networks that emulate the human brain
Book Description
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'll then learn how to classify data using techniques such as knearest 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.
What you will learn
 Create your first AI game in Python with the minmax algorithm
 Implement regression techniques to simplify realworld data
 Experiment with classification techniques to label realworld data
 Perform predictive analysis in Python using decision trees and random forests
 Use clustering algorithms to group data without manual support
 Learn how to use neural networks to process and classify labeled images
Who this book is for
The Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowledge of high schoollevel mathematics and at least one programming language, preferably Python. Although this is a beginner's book, experienced students and programmers can improve their Python skills by implementing the practical applications given in this book.
Publisher resources
Table of contents
 The Applied Artificial Intelligence Workshop
 Preface

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
 Intelligent Agents in Games
 Breadth First Search and Depth First Search
 Exploring the State Space of a Game
 Estimating the Number of Possible States in a TicTacToe Game
 Exercise 1.02: Creating an AI with Random Behavior for the TicTacToe Game
 Activity 1.01: Generating All Possible Sequences of Steps in a TicTacToe Game
 Exercise 1.03: Teaching the Agent to Win
 Defending the AI against Losses
 Activity 1.02: Teaching the Agent to Realize Situations When It Defends Against Losses
 Activity 1.03: Fixing the First and Second Moves of the AI to Make It Invincible
 Heuristics
 Pathfinding with the A* Algorithm
 Introducing the A* Algorithm
 Game AI with the Minmax Algorithm and AlphaBeta Pruning
 The Minmax Algorithm
 DRYing Up the Minmax Algorithm – the NegaMax Algorithm
 Summary

2. An Introduction to Regression
 Introduction

Linear Regression with One Variable
 Types of Regression
 Features and Labels
 Feature Scaling
 Splitting Data into Training and Testing
 Fitting a Model on Data with scikitlearn
 Linear Regression Using NumPy Arrays
 Fitting a Model Using NumPy Polyfit
 Predicting Values with Linear Regression
 Exercise 2.01: Predicting the Student Capacity of an Elementary School

Linear Regression with Multiple Variables
 Multiple Linear Regression
 The Process of Linear Regression
 Importing Data from Data Sources
 Loading Stock Prices with Yahoo Finance
 Exercise 2.02: Using Quandl to Load Stock Prices
 Preparing Data for Prediction
 Exercise 2.03: Preparing the Quandl Data for Prediction
 Performing and Validating Linear Regression
 Predicting the Future
 Polynomial and Support Vector Regression
 Support Vector Regression
 Summary

3. An Introduction to Classification
 Introduction
 The Fundamentals of Classification
 Data Preprocessing

The KNearest Neighbors Classifier
 Introducing the KNearest Neighbors Algorithm (KNN)
 Distance Metrics With KNearest Neighbors Classifier in ScikitLearn
 The Manhattan/Hamming Distance
 Exercise 3.03: Illustrating the KNearest Neighbors Classifier Algorithm in Matplotlib
 Parameterization of the KNearest Neighbors Classifier in scikitlearn
 Exercise 3.04: KNearest Neighbors Classification in scikitlearn
 Activity 3.01: Increasing the Accuracy of Credit Scoring
 Classification with Support Vector Machines
 Summary
 4. An Introduction to Decision Trees

5. Artificial Intelligence: Clustering
 Introduction
 Defining the Clustering Problem
 Clustering Approaches
 The KMeans Algorithm
 The Mean Shift Algorithm

Clustering Performance Evaluation
 The Adjusted Rand Index
 The Adjusted Mutual Information
 The VMeasure, Homogeneity, and Completeness
 The FowlkesMallows Score
 The Contingency Matrix
 The Silhouette Coefficient
 The CalinskiHarabasz Index
 The DaviesBouldin Index
 Activity 5.02: Clustering Red Wine Data Using the Mean Shift Algorithm and Agglomerative Hierarchical Clustering
 Summary

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
Product information
 Title: The Applied Artificial Intelligence Workshop
 Author(s):
 Release date: July 2020
 Publisher(s): Packt Publishing
 ISBN: 9781800205819
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