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
- Title:
- 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)
- Main Title:
- Artificial intelligence with Python build real-world artificial intelligence applications with Python to intelligently interact with the world around you
- 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 confidenceCover ; 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 … (more)
- 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.
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- 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.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.119451
- Ingest File:
- 01_033.xml