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Artificial intelligence with Python : build real-world artificial intelligence applications with Python to intelligently interact with the world around you /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.