Artificial intelligence with Python build real-world artificial intelligence applications with Python to intelligently interact with the world around you /: build real-world artificial intelligence applications with Python to intelligently interact with the world around you. (2017)

• Record Type: Book
• Further Information: Note: Prateek Joshi.
• Other Names: Joshi, Prateek
• Contents: Cover ; 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 Loading 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 Chapter 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 Chapter 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 Building 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
• Publisher Details: Birmingham, UK : Packt Publishing
• Publication Date: 2017
• Extent: 1 online resource
• Subjects: 006.3; COMPUTERS -- Programming Languages -- Python; Python (Computer program language); Artificial intelligence -- Data processing; Application software -- Development; COMPUTERS -- Intelligence (AI) & Semantics; COMPUTERS -- Programming -- Algorithms; Electronic books; Electronic books
• Languages: English
• ISBNs: 1786469677; 9781786469670
• Related ISBNs: 178646439X; 9781786464392
• Notes: Note: Print version record.
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• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.