book

The Applied Artificial Intelligence Workshop

Name: The Applied Artificial Intelligence Workshop
ISBN: 9781800205819

by Anthony So, William So, Zsolt Nagy, Swanand Bagve, John Wesley Doyle, Ashish Pratik Patil, Shantanu Shivrup Pathak, Subhranil Roy

July 2020

Beginner to intermediate

420 pages

7h 28m

English

Packt Publishing

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The Applied Artificial Intelligence Workshop
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentInstalling Jupyter on Your SystemLaunching the Jupyter NotebookInstalling LibrariesA Few Important PackagesAccessing the Code Files
1. Introduction to Artificial Intelligence
IntroductionHow Does AI Solve Problems?Diversity of Disciplines in AIFields and Applications of AISimulation of Human BehaviorSimulating Intelligence – the Turing TestWhat Disciplines Do We Need to Pass the Turing Test?AI Tools and Learning ModelsIntelligent AgentsThe Role of Python in AIWhy Is Python Dominant in Machine Learning, Data Science, and AI? Anaconda in PythonPython Libraries for AIA Brief Introduction to the NumPy LibraryExercise 1.01: Matrix Operations Using NumPyPython for Game AIIntelligent Agents in GamesBreadth First Search and Depth First SearchBreadth First Search (BFS)Depth First Search (DFS)Exploring the State Space of a GameEstimating the Number of Possible States in a Tic-Tac-Toe GameExercise 1.02: Creating an AI with Random Behavior for the Tic-Tac-Toe GameActivity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe GameExercise 1.03: Teaching the Agent to WinDefending the AI against LossesActivity 1.02: Teaching the Agent to Realize Situations When It Defends Against LossesActivity 1.03: Fixing the First and Second Moves of the AI to Make It InvincibleHeuristicsUninformed and Informed SearchesCreating HeuristicsAdmissible and Non-Admissible HeuristicsHeuristic EvaluationHeuristic 1: Simple Evaluation of the EndgameHeuristic 2: Utility of a Move Exercise 1.04: Tic-Tac-Toe Static Evaluation with a Heuristic FunctionUsing Heuristics for an Informed SearchTypes of HeuristicsPathfinding with the A* AlgorithmExercise 1.05: Finding the Shortest Path Using BFSIntroducing the A* AlgorithmA* Search in Practice Using the simpleai LibraryGame AI with the Minmax Algorithm and Alpha-Beta Pruning Search Algorithms for Turn-Based Multiplayer GamesThe Minmax AlgorithmOptimizing the Minmax Algorithm with Alpha-Beta PruningDRYing Up the Minmax Algorithm – the NegaMax AlgorithmUsing the EasyAI Library Activity 1.04: Connect FourSummary
2. An Introduction to Regression
IntroductionLinear Regression with One VariableTypes of RegressionFeatures and LabelsFeature ScalingSplitting Data into Training and TestingFitting a Model on Data with scikit-learn Linear Regression Using NumPy ArraysFitting a Model Using NumPy PolyfitPlotting the Results in PythonPredicting Values with Linear RegressionExercise 2.01: Predicting the Student Capacity of an Elementary SchoolLinear Regression with Multiple VariablesMultiple Linear RegressionThe Process of Linear RegressionImporting Data from Data SourcesLoading Stock Prices with Yahoo FinanceExercise 2.02: Using Quandl to Load Stock PricesPreparing Data for PredictionExercise 2.03: Preparing the Quandl Data for PredictionPerforming and Validating Linear RegressionPredicting the FuturePolynomial and Support Vector RegressionPolynomial Regression with One VariableExercise 2.04: First-, Second-, and Third-Degree Polynomial RegressionPolynomial Regression with Multiple VariablesSupport Vector RegressionSupport Vector Machines with a 3-Degree Polynomial KernelActivity 2.01: Boston House Price Prediction with Polynomial Regression of Degrees 1, 2, and 3 on Multiple VariablesSummary
3. An Introduction to Classification
IntroductionThe Fundamentals of ClassificationExercise 3.01: Predicting Risk of Credit Card Default (Loading the Dataset)Data PreprocessingExercise 3.02: Applying Label Encoding to Transform Categorical Variables into Numerical VariablesIdentifying Features and LabelsSplitting Data into Training and Testing Using Scikit-LearnThe K-Nearest Neighbors ClassifierIntroducing the K-Nearest Neighbors Algorithm (KNN)Distance Metrics With K-Nearest Neighbors Classifier in Scikit-LearnThe Euclidean DistanceThe Manhattan/Hamming DistanceExercise 3.03: Illustrating the K-Nearest Neighbors Classifier Algorithm in MatplotlibParameterization of the K-Nearest Neighbors Classifier in scikit-learnExercise 3.04: K-Nearest Neighbors Classification in scikit-learnActivity 3.01: Increasing the Accuracy of Credit ScoringClassification with Support Vector MachinesWhat Are Support Vector Machine Classifiers?Understanding Support Vector MachinesSupport Vector Machines in scikit-learnParameters of the scikit-learn SVMActivity 3.02: Support Vector Machine Optimization in scikit-learnSummary
4. An Introduction to Decision Trees
IntroductionDecision TreesEntropyExercise 4.01: Calculating EntropyInformation GainGini ImpurityExit ConditionBuilding Decision Tree Classifiers Using scikit-learnPerformance Metrics for ClassifiersExercise 4.02: Precision, Recall, and F1 Score CalculationEvaluating the Performance of Classifiers with scikit-learnThe Confusion MatrixActivity 4.01: Car Data ClassificationRandom Forest ClassifierRandom Forest Classification Using scikit-learnThe Parameterization of the Random Forest ClassifierFeature ImportanceCross-ValidationExtremely Randomized TreesActivity 4.02: Random Forest Classification for Your Car Rental CompanySummary
5. Artificial Intelligence: Clustering
IntroductionDefining the Clustering ProblemClustering ApproachesClustering Algorithms Supported by scikit-learnThe K-Means AlgorithmExercise 5.01: Implementing K-Means in scikit-learnThe Parameterization of the K-Means Algorithm in scikit-learnExercise 5.02: Retrieving the Center Points and the LabelsK-Means Clustering of Sales DataActivity 5.01: Clustering Sales Data Using K-MeansThe Mean Shift AlgorithmExercise 5.03: Implementing the Mean Shift AlgorithmThe Mean Shift Algorithm in scikit-learnHierarchical ClusteringAgglomerative Hierarchical Clustering in scikit-learnClustering Performance EvaluationThe Adjusted Rand IndexThe Adjusted Mutual InformationThe V-Measure, Homogeneity, and Completeness The Fowlkes-Mallows ScoreThe Contingency MatrixThe Silhouette CoefficientThe Calinski-Harabasz IndexThe Davies-Bouldin IndexActivity 5.02: Clustering Red Wine Data Using the Mean Shift Algorithm and Agglomerative Hierarchical ClusteringSummary
6. Neural Networks and Deep Learning
IntroductionArtificial NeuronsNeurons in TensorFlowExercise 6.01: Using Basic Operations and TensorFlow ConstantsNeural Network ArchitectureWeightsBiasesUse Cases for ANNsActivation FunctionsSigmoidTanhReLUSoftmaxExercise 6.02: Activation FunctionsForward Propagation and the Loss FunctionBackpropagationOptimizers and the Learning RateExercise 6.03: Classifying Credit ApprovalRegularizationExercise 6.04: Predicting Boston House Prices with RegularizationActivity 6.01: Finding the Best Accuracy Score for the Digits DatasetDeep LearningShallow versus Deep NetworksComputer Vision and Image ClassificationConvolutional Neural Networks (CNNs)Convolutional OperationsPooling LayerCNN ArchitectureActivity 6.02: Evaluating a Fashion Image Recognition Model Using CNNsRecurrent Neural Networks (RNNs)RNN LayersThe GRU LayerThe LSTM LayerActivity 6.03: Evaluating a Yahoo Stock Model with an RNNHardware for Deep LearningChallenges and Future TrendsSummary
Appendix
1. Introduction to Artificial IntelligenceActivity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe GameActivity 1.02: Teaching the Agent to Realize Situations When It Defends Against LossesActivity 1.03: Fixing the First and Second Moves of the AI to Make It InvincibleActivity 1.04: Connect Four2. An Introduction to RegressionActivity 2.01: Boston House Price Prediction with Polynomial Regression of Degrees 1, 2, and 3 on Multiple Variables3. An Introduction to ClassificationActivity 3.01: Increasing the Accuracy of Credit ScoringActivity 3.02: Support Vector Machine Optimization in scikit-learn4. An Introduction to Decision TreesActivity 4.01: Car Data ClassificationActivity 4.02: Random Forest Classification for Your Car Rental Company5. Artificial Intelligence: ClusteringActivity 5.01: Clustering Sales Data Using K-MeansActivity 5.02: Clustering Red Wine Data Using the Mean Shift Algorithm and Agglomerative Hierarchical Clustering6. Neural Networks and Deep LearningActivity 6.01: Finding the Best Accuracy Score for the Digits DatasetActivity 6.02: Evaluating a Fashion Image Recognition Model Using CNNsActivity 6.03: Evaluating a Yahoo Stock Model with an RNN

Content preview from The Applied Artificial Intelligence Workshop

3. An Introduction to Classification

Overview

This chapter introduces you to classification. You will implement various techniques, such as k-nearest neighbors and SVMs. You will use the Euclidean and Manhattan distances to work with k-nearest neighbors. You will apply these concepts to solve intriguing problems such as predicting whether a credit card applicant has a risk of defaulting and determining whether an employee would stay with a company for more than two years. By the end of this chapter, you will be confident enough to work with any data using classification and come to a certain conclusion.

Introduction

In the previous chapter, you were introduced to regression models and learned how to fit a linear regression model with single ...

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The Applied Artificial Intelligence Workshop

by Anthony So, William So, Zsolt Nagy, Swanand Bagve, John Wesley Doyle, Ashish Pratik Patil, Shantanu Shivrup Pathak, Subhranil Roy

3. An Introduction to Classification

Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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