The Applied Artificial Intelligence Workshop: Start working with AI today, to build games, design decision trees, and train your own machine learning models

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 FeaturesLearn about AI and ML algorithms from the perspective of a seasoned data scientistGet practical experience in ML algorithms, such as regression, tree algorithms, clustering, and moreDesign neural networks that emulate the human brainBook 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 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. What you will learnCreate your first AI game in Python with the minmax algorithmImplement regression techniques to simplify real-world dataExperiment with classification techniques to label real-world dataPerform predictive analysis in Python using decision trees and random forestsUse clustering algorithms to group data without manual supportLearn how to use neural networks to process and classify labeled imagesWho 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 school-level 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. Table of ContentsIntroduction to Artificial IntelligenceAn Introduction to RegressionAn Introduction to ClassificationAn Introduction to Decision TreesArtificial Intelligence: ClusteringNeural Networks and Deep Learning Cover FM Copyright Table of Contents Preface Chapter 1: Introduction to Artificial Intelligence Introduction How Does AI Solve Problems? Diversity of Disciplines in AI Fields and Applications of AI Simulation of Human Behavior Simulating Intelligence – the Turing Test What Disciplines Do We Need to Pass the Turing Test? AI Tools and Learning Models Intelligent Agents The Role of Python in AI Why Is Python Dominant in Machine Learning, Data Science, and AI? Anaconda in Python Python Libraries for AI A Brief Introduction to the NumPy Library Exercise 1.01: Matrix Operations Using NumPy Python for Game AI Intelligent Agents in Games Breadth First Search and Depth First Search Breadth First Search Depth First Search (DFS) Exploring the State Space of a Game Estimating the Number of Possible States in a Tic-Tac-Toe Game Exercise 1.02: Creating an AI with Random Behavior for the Tic-Tac-Toe Game Activity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe 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 Uninformed and Informed Searches Creating Heuristics Admissible and Non-Admissible Heuristics Heuristic Evaluation Heuristic 1: Simple Evaluation of the Endgame Heuristic 2: Utility of a Move Exercise 1.04: Tic-Tac-Toe Static Evaluation with a Heuristic Function Using Heuristics for an Informed Search Types of Heuristics Pathfinding with the A* Algorithm Exercise 1.05: 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 1.04: Connect Four Summary Chapter 2: An Introductionto 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 scikit-learn Linear Regression Using NumPy Arrays Fitting a Model Using NumPy Polyfit Plotting the Results in Python 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 Polynomial Regression with One Variable Exercise 2.04: First-, Second-, and Third-Degree Polynomial Regression Polynomial Regression with Multiple Variables Support Vector Regression Support Vector Machines with a 3-Degree Polynomial Kernel Activity 2.01: Boston House Price Prediction with Polynomial Regression of Degrees 1, 2, and 3 on Multiple Variables Summary Chapter 3: An Introduction to Classification Introduction The Fundamentals of Classification Exercise 3.01: Predicting Risk of Credit Card Default (Loading the Dataset) Data Preprocessing Exercise 3.02: Applying Label Encoding to Transform Categorical Variables into Numerical variables Identifying Features and Labels Splitting Data into Training and Testing Using Scikit-Learn The K-Nearest Neighbors Classifier Introducing the K-Nearest Neighbors Algorithm (KNN) Distance Metrics With K-Nearest Neighbors Classifier in Scikit-Learn The Euclidean Distance The Manhattan/Hamming Distance Exercise 3.03: Illustrating the K-Nearest Neighbors Classifier Algorithm in Matplotlib Parameterization of the K-Nearest Neighbors Classifier in scikit-learn Exercise 3.04: K-Nearest Neighbors Classification in scikit-learn Activity 3.01: 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 3.02: Support Vector Machine Optimization in scikit-learn Summary Chapter 4: An Introduction to Decision Trees Introduction Decision Trees Entropy Exercise 4.01: Calculating Entropy Information Gain Gini Impurity Exit Condition Building Decision Tree Classifiers Using scikit-learn Performance Metrics for Classifiers Exercise 4.02: Precision, Recall, and F1 Score Calculation Evaluating the Performance of Classifiers with scikit-learn The Confusion Matrix Activity 4.01: Car Data Classification Random Forest Classifier Random Forest Classification Using scikit-learn The Parameterization of the Random Forest Classifier Feature Importance Cross-Validation Extremely Randomized Trees Activity 4.02: Random Forest Classification for Your Car Rental Company Summary Chapter 5: Artificial Intelligence: Clustering Introduction Defining the Clustering Problem Clustering Approaches Clustering Algorithms Supported by scikit-learn The K-Means Algorithm Exercise 5.01: Implementing K-Means in scikit-learn The Parameterization of the K-Means Algorithm in scikit-learn Exercise 5.02: Retrieving the Center Points and the Labels K-Means Clustering of Sales Data Activity 5.01: Clustering Sales Data Using K-Means The Mean Shift Algorithm Exercise 5.03: Implementing the Mean Shift Algorithm The Mean Shift Algorithm in scikit-learn Hierarchical Clustering Agglomerative Hierarchical Clustering in scikit-learn Clustering Performance Evaluation The Adjusted Rand Index The Adjusted Mutual Information The V-Measure, Homogeneity, and Completeness The Fowlkes-Mallows Score The Contingency Matrix The Silhouette Coefficient The Calinski-Harabasz Index The Davies-Bouldin Index Activity 5.02: Clustering Red Wine Data Using the Mean Shift Algorithm and Agglomerative Hierarchical Clustering Summary Chapter 6: Neural Networks and Deep Learning Introduction Artificial Neurons Neurons in TensorFlow Exercise 6.01: Using Basic Operations and TensorFlow Constants Neural Network Architecture Weights Biases Use Cases for ANNs Activation Functions Sigmoid Tanh ReLU Softmax Exercise 6.02: Activation Functions Forward Propagation and the Loss Function Backpropagation Optimizers and the Learning Rate Exercise 6.03: Classifying Credit Approval Regularization Exercise 6.04: Predicting Boston House Prices with Regularization Activity 6.01: Finding the Best Accuracy Score for the Digits Dataset Deep Learning Shallow versus Deep Networks Computer Vision and Image Classification Convolutional Neural Networks (CNNs) Convolutional Operations Pooling Layer CNN Architecture Activity 6.02: Evaluating a Fashion Image Recognition Model Using CNNs Recurrent Neural Networks (RNNs) RNN Layers The GRU Layer The LSTM Layer Activity 6.03: Evaluating a Yahoo Stock Model with an RNN Hardware for Deep Learning Challenges and Future Trends Summary Appendix Index