MARC Record from marc_columbia

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035 $a(NNC)13632348
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020 $a1786469677$q(electronic bk.)
020 $a9781786469670$q(electronic bk.)
020 $z178646439X
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037 $a9781786469670$bPackt Publishing Pvt. Ltd
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100 1 $aJoshi, Prateek.
245 10 $aArtificial intelligence with Python :$bbuild real-world artificial intelligence applications with Python to intelligently interact with the world around you /$cPrateek Joshi.
260 $aBirmingham, UK :$bPackt Publishing,$c2017.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
588 0 $aPrint version record.
505 0 $aCover ; Copyright ; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Introduction to Artificial Intelligence ; What is Artificial Intelligence?; Why do we need to study AI?; Applications of AI; Branches of AI; Defining intelligence using Turing Test; Making machines think like humans; Building rational agents; General Problem Solver; Solving a problem with GPS; Building an intelligent agent; Types of models; Installing Python 3; Installing on Ubuntu; Installing on Mac OS X; Installing on Windows; Installing packages.
505 8 $aLoading dataSummary; Chapter 2 : Classification and Regression Using Supervised Learning; Supervised versus unsupervised learning; What is classification?; Preprocessing data; Binarization; Mean removal; Scaling; Normalization; Label encoding; Logistic Regression classifier; Naïve Bayes classifier; Confusion matrix; Support Vector Machines; Classifying income data using Support Vector Machines; What is Regression?; Building a single variable regressor; Building a multivariable regressor; Estimating housing prices using a Support Vector Regressor; Summary.
505 8 $aChapter 3:Predictive Analytics with Ensemble Learning What is Ensemble Learning?; Building learning models with Ensemble Learning; What are Decision Trees?; Building a Decision Tree classifier; What are Random Forests and Extremely Random Forests?; Building Random Forest and Extremely Random Forest classifiers; Estimating the confidence measure of the predictions; Dealing with class imbalance; Finding optimal training parameters using grid search; Computing relative feature importance; Predicting traffic using Extremely Random Forest regressor; Summary.
505 8 $aChapter 4:Detecting Patterns with Unsupervised Learning What is unsupervised learning?; Clustering data with K-Means algorithm; Estimating the number of clusters with Mean Shift algorithm; Estimating the quality of clustering with silhouette scores; What are Gaussian Mixture Models?; Building a classifier based on Gaussian Mixture Models; Finding subgroups in stock market using Affinity Propagation model; Segmenting the market based on shopping patterns; Summary; Chapter 5: Building Recommender Systems ; Creating a training pipeline; Extracting the nearest neighbors.
505 8 $aBuilding a K-Nearest Neighbors classifierComputing similarity scores; Finding similar users using collaborative filtering; Building a movie recommendation system; Summary; Chapter 6: Logic Programming ; What is logic programming?; Understanding the building blocks of logic programming; Solving problems using logic programming; Installing Python packages; Matching mathematical expressions; Validating primes; Parsing a family tree; Analyzing geography; Building a puzzle solver; Summary; Chapter 7: Heuristic Search Techniques ; What is heuristic search?; Uninformed versus Informed search.
520 8 $aAnnotation$bBuild real-world Artificial Intelligence applications with Python to intelligently interact with the world around youAbout This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approachThis highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
650 0 $aPython (Computer program language)
650 0 $aArtificial intelligence$xData processing.
650 0 $aApplication software$xDevelopment.
650 6 $aPython (Langage de programmation)
650 6 $aIntelligence artificielle$xInformatique.
650 6 $aLogiciels d'application$xDéveloppement.
650 7 $aCOMPUTERS$xProgramming Languages$xPython.$2bisacsh
650 7 $aApplication software$xDevelopment.$2fast$0(OCoLC)fst00811707
650 7 $aArtificial intelligence$xData processing.$2fast$0(OCoLC)fst00817255
650 7 $aPython (Computer program language)$2fast$0(OCoLC)fst01084736
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aJoshi, Prateek.$tArtificial intelligence with Python.$dBirmingham, UK : Packt Publishing, 2017$z9781786464392$w(OCoLC)956749331
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio13632348$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS