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by Zsolt Nagy
Artificial Intelligence and Machine Learning Fundamentals
Preface
About the Book
About the Author
Objectives
Audience
Approach
Minimum Hardware Requirements
Software Requirements
Conventions
Installation and Setup
Starting Anaconda
Additional Resources
Principles of Artificial Intelligence
Introduction
How does AI Solve Real World Problems?
Diversity of Disciplines
Fields and Applications of Artificial Intelligence
Simulating Intelligence – The Turing Test
AI Tools and Learning Models
Classification and Prediction
Learning Models
The Role of Python in Artificial Intelligence
Why is Python Dominant in Machine Learning, Data Science, and AI?
Anaconda in Python
Python Libraries for Artificial Intelligence
A Brief Introduction to the NumPy Library
Exercise 1: Matrix Operations Using NumPy
Python for Game AI
Intelligent Agents in Games
Breadth First Search and Depth First Search
Exploring the State Space of a Game
Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game
Exercise 3: Creating an AI Randomly
Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game
Summary
AI with Search Techniques and Games
Introduction
Exercise 4: Teaching the Agent to Win
Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses
Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible
Heuristics
Uninformed and Informed Search
Creating Heuristics
Admissible and Non-Admissible Heuristics
Heuristic Evaluation
Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function
Using Heuristics for an Informed Search
Types of Heuristics
Pathfinding with the A* Algorithm
Exercise 6: Finding the Shortest Path to Reach a Goal
Exercise 7: Finding the Shortest Path Using BFS
Introducing the A* Algorithm
A* Search in Practice Using the simpleai Library
Game AI with the Minmax Algorithm and Alpha-Beta Pruning
Search Algorithms for Turn-Based Multiplayer Games
The Minmax Algorithm
Optimizing the Minmax Algorithm with Alpha-Beta Pruning
DRYing up the Minmax Algorithm – The NegaMax Algorithm
Using the EasyAI Library
Activity 4: Connect Four
Summary
Regression
Introduction
Linear Regression with One Variable
What Is Regression?
Features and Labels
Feature Scaling
Cross-Validation with Training and Test Data
Fitting a Model on Data with scikit-learn
Linear Regression Using NumPy Arrays
Fitting a Model Using NumPy Polyfit
Predicting Values with Linear Regression
Activity 5: Predicting Population
Linear Regression with Multiple Variables
Multiple Linear Regression
The Process of Linear Regression
Importing Data from Data Sources
Loading Stock Prices with Yahoo Finance
Loading Files with pandas
Loading Stock Prices with Quandl
Exercise 8: Using Quandl to Load Stock Prices
Preparing Data for Prediction
Performing and Validating Linear Regression
Predicting the Future
Polynomial and Support Vector Regression
Polynomial Regression with One Variable
Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression
Polynomial Regression with Multiple Variables
Support Vector Regression
Support Vector Machines with a 3 Degree Polynomial Kernel
Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables
Summary
Classification
Introduction
The Fundamentals of Classification
Exercise 10: Loading Datasets
Data Preprocessing
Exercise 11: Pre-Processing Data
Minmax Scaling of the Goal Column
Identifying Features and Labels
Cross-Validation with scikit-learn
Activity 7: Preparing Credit Data for Classification
The k-nearest neighbor Classifier
Introducing the K-Nearest Neighbor Algorithm
Distance Functions
Exercise 12: Illustrating the K-nearest Neighbor Classifier Algorithm
Exercise 13: k-nearest Neighbor Classification in scikit-learn
Exercise 14: Prediction with the k-nearest neighbors classifier
Parameterization of the k-nearest neighbor Classifier in scikit-learn
Activity 8: Increasing the Accuracy of Credit Scoring
Classification with Support Vector Machines
What are Support Vector Machine Classifiers?
Understanding Support Vector Machines
Support Vector Machines in scikit-learn
Parameters of the scikit-learn SVM
Activity 9: Support Vector Machine Optimization in scikit-learn
Summary
Using Trees for Predictive Analysis
Introduction to Decision Trees
Entropy
Exercise 15: Calculating the Entropy
Information Gain
Gini Impurity
Exit Condition
Building Decision Tree Classifiers using scikit-learn
Evaluating the Performance of Classifiers
Exercise 16: Precision and Recall
Exercise 17: Calculating the F1 Score
Confusion Matrix
Exercise 18: Confusion Matrix
Activity 10: Car Data Classification
Random Forest Classifier
Constructing a Random Forest
Random Forest Classification Using scikit-learn
Parameterization of the random forest classifier
Feature Importance
Extremely Randomized Trees
Activity 11: Random Forest Classification for Your Car Rental Company
Summary
Clustering
Introduction to Clustering
Defining the Clustering Problem
Clustering Approaches
Clustering Algorithms Supported by scikit-learn
The k-means Algorithm
Exercise 19: k-means in scikit-learn
Parameterization of the k-means Algorithm in scikit-learn
Exercise 20: Retrieving the Center Points and the Labels
k-means Clustering of Sales Data
Activity 12: k-means Clustering of Sales Data
Mean Shift Algorithm
Exercise 21: Illustrating Mean Shift in 2D
Mean Shift Algorithm in scikit-learn
Image Processing in Python
Activity 13: Shape Recognition with the Mean Shift Algorithm
Summary
Deep Learning with Neural Networks
Introduction
TensorFlow for Python
Installing TensorFlow in the Anaconda Navigator
TensorFlow Operations
Exercise 22: Using Basic Operations and TensorFlow constants
Placeholders and Variables
Global Variables Initializer
Introduction to Neural Networks
Biases
Use Cases for Artificial Neural Networks
Activation Functions
Exercise 23: Activation Functions
Forward and Backward Propagation
Configuring a Neural Network
Importing the TensorFlow Digit Dataset
Modeling Features and Labels
TensorFlow Modeling for Multiple Labels
Optimizing the Variables
Training the TensorFlow Model
Using the Model for Prediction
Testing the Model
Randomizing the Sample Size
Activity 14: Written Digit Detection
Deep Learning
Adding Layers
Convolutional Neural Networks
Activity 15: Written Digit Detection with Deep Learning
Summary
Appendix
Chapter 1: Principles of AI
Activity 1: Generating All Possible Sequences of Steps in the tic-tac-toe Game
Chapter 2: AI with Search Techniques and Games
Activity 2: Teach the agent realize situations when it defends against losses
Activity 3: Fix the first and second moves of the AI to make it invincible
Activity 4: Connect Four
Chapter 3: Regression
Activity 5: Predicting Population
Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables
Chapter 4: Classification
Activity 7: Preparing Credit Data for Classification
Activity 8: Increase the accuracy of credit scoring
Activity 9: Support Vector Machine Optimization in scikit-learn
Chapter 5: Using Trees for Predictive Analysis
Activity 10: Car Data Classification
Activity 11: Random Forest Classification for your Car Rental Company
Chapter 6: Clustering
Activity 12: k-means Clustering of Sales Data
Activity 13: Shape Recognition with the Mean Shift algorithm
Chapter 7: Deep Learning with Neural Networks
Activity 14: Written digit detection
Activity 15 : Written Digit Detection with Deep Learning
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