book

Artificial Intelligence with Python - Second Edition

Name: Artificial Intelligence with Python - Second Edition
ISBN: 9781839219535

by Alberto Artasanchez, Prateek Joshi

January 2020

Beginner to intermediate

618 pages

13h 57m

English

Packt Publishing

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Preface
Who this book is forWhat this book coversWhat you need for this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Introduction to Artificial Intelligence
What is AI?Why do we need to study AI?Branches of AIThe five tribes of machine learningDefining intelligence using the Turing testMaking machines think like humansBuilding rational agentsGeneral Problem SolverSolving a problem with GPSBuilding an intelligent agentTypes of modelsInstalling Python 3Installing on UbuntuInstalling on Mac OS XInstalling on WindowsInstalling packagesLoading dataSummary
Fundamental Use Cases for Artificial Intelligence
Representative AI use casesDigital personal assistants and chatbotsPersonal chauffeurShipping and warehouse managementHuman healthKnowledge searchRecommendation systemsThe smart homeGamingMovie makingUnderwriting and deal analysisData cleansing and transformationSummaryReferences
Machine Learning Pipelines
What is a machine learning pipeline?Problem definitionData ingestionData preparationMissing valuesDuplicate records or valuesFeature scalingInconsistent valuesInconsistent date formattingData segregationModel trainingCandidate model evaluation and selectionModel deploymentPerformance monitoringModel performanceOperational performanceTotal cost of ownership (TCO)Service performanceSummary
Feature Selection and Feature Engineering
Feature selectionFeature importanceUnivariate selectionCorrelation heatmapsWrapper-based methodsFilter-based methodsEmbedded methodsFeature engineeringImputationOutlier managementOne-hot encodingLog transformScalingDate manipulationSummary
Classification and Regression Using Supervised Learning
Supervised versus unsupervised learningWhat is classification?Preprocessing dataBinarizationMean removalScalingNormalizationLabel encodingLogistic regression classifiersThe Naïve Bayes classifierConfusion matrixesSupport Vector MachinesClassifying income data using Support Vector MachinesWhat is regression?Building a single-variable regressorBuilding a multivariable regressorEstimating housing prices using a Support Vector RegressorSummary
Predictive Analytics with Ensemble Learning
What are decision trees?Building a decision tree classifierWhat is ensemble learning?Building learning models with ensemble learningWhat are random forests and extremely random forests?Building random forest and extremely random forest classifiersEstimating the confidence measure of the predictionsDealing with class imbalanceFinding optimal training parameters using grid searchComputing relative feature importancePredicting traffic using an extremely random forest regressorSummary
Detecting Patterns with Unsupervised Learning
What is unsupervised learning?Clustering data with the K-Means algorithmEstimating the number of clusters with the Mean Shift algorithmEstimating the quality of clustering with silhouette scoresWhat are Gaussian Mixture Models?Building a classifier based on Gaussian Mixture ModelsFinding subgroups in stock market using the Affinity Propagation modelSegmenting the market based on shopping patternsSummary
Building Recommender Systems
Extracting the nearest neighborsBuilding a K-nearest neighbors classifierComputing similarity scoresFinding similar users using collaborative filteringBuilding a movie recommendation systemSummary
Logic Programming
What is logic programming?Understanding the building blocks of logic programmingSolving problems using logic programmingInstalling Python packagesMatching mathematical expressionsValidating primesParsing a family treeAnalyzing geographyBuilding a puzzle solverSummary

Heuristic Search Techniques
Is heuristic search artificial intelligence?What is heuristic search?Uninformed versus informed searchConstraint satisfaction problemsLocal search techniquesSimulated annealingConstructing a string using greedy searchSolving a problem with constraintsSolving the region-coloring problemBuilding an 8-puzzle solverBuilding a maze solverSummary
Genetic Algorithms and Genetic Programming
The evolutionists tribeUnderstanding evolutionary and genetic algorithmsFundamental concepts in genetic algorithmsGenerating a bit pattern with predefined parametersVisualizing the evolutionSolving the symbol regression problemBuilding an intelligent robot controllerGenetic programming use casesSummaryReferences
Artificial Intelligence on the Cloud
Why are companies migrating to the cloud?The top cloud providersAmazon Web Services (AWS)Amazon SageMakerAlexa, Lex, and Polly – conversational gentsAmazon Comprehend – natural language processingAmazon Rekognition – image and videoAmazon TranslateAmazon Machine LearningAmazon Transcribe – transcriptionAmazon Textract – document analysisMicrosoft AzureMicrosoft Azure Machine Learning StudioAzure Machine Learning ServiceAzure Cognitive ServicesGoogle Cloud Platform (GCP)AI HubGoogle Cloud AI Building BlocksSummary
Building Games with Artificial Intelligence
Using search algorithms in gamesCombinatorial searchThe Minimax algorithmAlpha-Beta pruningThe Negamax algorithmInstalling the easyAI libraryBuilding a bot to play Last Coin StandingBuilding a bot to play Tic-Tac-ToeBuilding two bots to play Connect Four™ against each otherBuilding two bots to play Hexapawn against each otherSummary
Building a Speech Recognizer
Working with speech signalsVisualizing audio signalsTransforming audio signals to the frequency domainGenerating audio signalsSynthesizing tones to generate musicExtracting speech featuresRecognizing spoken wordsSummary
Natural Language Processing
Introduction and installation of packagesTokenizing text dataConverting words to their base forms using stemmingConverting words to their base forms using lemmatizationDividing text data into chunksExtracting the frequency of terms using the Bag of Words modelBuilding a category predictorConstructing a gender identifierBuilding a sentiment analyzerTopic modeling using Latent Dirichlet AllocationSummary
Chatbots
The future of chatbotsChatbots todayChatbot conceptsA well-architected chatbotChatbot platformsCreating a chatbot using DialogFlowDialogFlow setupIntegrating a chatbot into a website using a widgetIntegrating a chatbot into a website using PythonHow to set up a webhook in DialogFlowEnabling webhooks for intentsSetting up training phrases for an intentSetting up parameters and actions for an intentBuilding fulfillment responses from a webhookChecking responses from a webhookSummary
Sequential Data and Time Series Analysis
Understanding sequential dataHandling time series data with PandasSlicing time series dataOperating on time series dataExtracting statistics from time series dataGenerating data using Hidden Markov ModelsIdentifying alphabet sequences with Conditional Random FieldsStock market analysisSummary
Image Recognition
Importance of image recognitionOpenCVFrame differencingTracking objects using color spacesObject tracking using background subtractionBuilding an interactive object tracker using the CAMShift algorithmOptical flow-based trackingFace detection and trackingUsing Haar cascades for object detectionUsing integral images for feature extractionEye detection and trackingSummary
Neural Networks
Introduction to neural networksBuilding a neural networkTraining a neural networkBuilding a Perceptron-based classifierConstructing a single-layer neural networkConstructing a multi-layer neural networkBuilding a vector quantizerAnalyzing sequential data using recurrent neural networksVisualizing characters in an optical character recognition databaseBuilding an optical character recognition engineSummary
Deep Learning with Convolutional Neural Networks
The basics of Convolutional Neural NetworksArchitecture of CNNsCNNs vs. perceptron neural networksTypes of layers in a CNNBuilding a perceptron-based linear regressorBuilding an image classifier using a single-layer neural networkBuilding an image classifier using a Convolutional Neural NetworkSummaryReference
Recurrent Neural Networks and Other Deep Learning Models
The basics of Recurrent Neural NetworksStep functionSigmoid functionTanh functionReLU functionArchitecture of RNNsA language modeling use caseTraining an RNNSummary
Creating Intelligent Agents with Reinforcement Learning
Understanding what it means to learnReinforcement learning versus supervised learningReal-world examples of reinforcement learningBuilding blocks of reinforcement learningCreating an environmentBuilding a learning agentSummary
Artificial Intelligence and Big Data
Big data basicsCrawlingIndexingRankingWorldwide datacentersDistributed lookupsCustom softwareThe three V's of big dataVolumeVelocityVarietyBig data and machine learningApache HadoopMapReduceApache HiveApache SparkResilient distributed datasetsDataFramesSparkSQLApache ImpalaNoSQL DatabasesTypes of NoSQL databasesApache CassandraMongoDBRedisNeo4jSummary
Other Books You May Enjoy
Index

Overview

Dive into the world of Artificial Intelligence with this comprehensive guide tailored for Python enthusiasts. Explore everything from the basics of AI to advanced topics such as recurrent neural networks, deep learning, and deploying AI solutions on the cloud. By the end of this book, you'll have the tools and knowledge needed to create intelligent applications using Python.

What this Book will help me do

Master the foundational concepts of Artificial Intelligence and its practical applications.
Learn to build and fine-tune machine learning pipelines for real-world projects.
Explore deep learning techniques including convolutional and recurrent neural networks.
Develop solutions employing AI in areas like natural language processing and image recognition.
Get hands-on experience deploying AI models on cloud platforms for scalability.

Author(s)

Alberto Artasanchez and None Joshi bring a wealth of experience in Artificial Intelligence and Python programming. Alberto is known for his practical approach to AI, blending theory with hands-on projects. None complements this with their expertise in machine learning and software engineering. Together, they aim to make AI accessible and enjoyable for every programmer.

Who is it for?

This book is tailored for Python developers who aim to harness the power of Artificial Intelligence in real-world applications. Whether you have a foundational understanding of Python or are already acquainted with machine learning basics, this guide will elevate your skills. If you aspire to innovate and create intelligent systems, this book is for you.

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Artificial Intelligence Programming with Python

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ISBN: 9781839219535

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