Artificial Intelligence with Python (ebook)(audiobook)(audiobook)

Spis treści książki

• Artificial Intelligence with Python
  • Artificial Intelligence with Python
  • Credits
  • About the Author
  • About the Reviewer
  • www.PacktPub.com
    • Why subscribe?
  • Customer Feedback
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Downloading the color images of this book
      • Errata
      • Piracy
      • Questions
  • 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 data
    • Summary
  • 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
    • Nave 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
  • 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
  • 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
  • 5. Building Recommender Systems
    • Creating a training pipeline
    • Extracting the nearest neighbors
    • Building a K-Nearest Neighbors classifier
    • Computing similarity scores
    • Finding similar users using collaborative filtering
    • Building a movie recommendation system
    • Summary
  • 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
  • 7. Heuristic Search Techniques
    • What is heuristic search?
      • Uninformed versus Informed search
    • Constraint Satisfaction Problems
    • Local search techniques
      • Simulated Annealing
    • Constructing a string using greedy search
    • Solving a problem with constraints
    • Solving the region-coloring problem
    • Building an 8-puzzle solver
    • Building a maze solver
    • Summary
  • 8. Genetic Algorithms
    • Understanding evolutionary and genetic algorithms
    • Fundamental concepts in genetic algorithms
    • Generating a bit pattern with predefined parameters
    • Visualizing the evolution
    • Solving the symbol regression problem
    • Building an intelligent robot controller
    • Summary
  • 9. Building Games With Artificial Intelligence
    • Using search algorithms in games
    • Combinatorial search
    • Minimax algorithm
    • Alpha-Beta pruning
    • Negamax algorithm
    • Installing easyAI library
    • Building a bot to play Last Coin Standing
    • Building a bot to play Tic-Tac-Toe
    • Building two bots to play Connect Four against each other
    • Building two bots to play Hexapawn against each other
    • Summary
  • 10. Natural Language Processing
    • Introduction and installation of packages
    • Tokenizing text data
    • Converting words to their base forms using stemming
    • Converting words to their base forms using lemmatization
    • Dividing text data into chunks
    • Extracting the frequency of terms using a Bag of Words model
    • Building a category predictor
    • Constructing a gender identifier
    • Building a sentiment analyzer
    • Topic modeling using Latent Dirichlet Allocation
    • Summary
  • 11. Probabilistic Reasoning for Sequential Data
    • Understanding sequential data
    • Handling time-series data with Pandas
    • Slicing time-series data
    • Operating on time-series data
    • Extracting statistics from time-series data
    • Generating data using Hidden Markov Models
    • Identifying alphabet sequences with Conditional Random Fields
    • Stock market analysis
    • Summary
  • 12. Building A Speech Recognizer
    • Working with speech signals
    • Visualizing audio signals
    • Transforming audio signals to the frequency domain
    • Generating audio signals
    • Synthesizing tones to generate music
    • Extracting speech features
    • Recognizing spoken words
    • Summary
  • 13. Object Detection and Tracking
    • Installing OpenCV
    • Frame differencing
    • Tracking objects using colorspaces
    • Object tracking using background subtraction
    • Building an interactive object tracker using the CAMShift algorithm
    • Optical flow based tracking
    • Face detection and tracking
      • Using Haar cascades for object detection
      • Using integral images for feature extraction
    • Eye detection and tracking
    • Summary
  • 14. Artificial Neural Networks
    • Introduction to artificial neural networks
      • Building a neural network
      • Training a neural network
    • Building a Perceptron based classifier
    • Constructing a single layer neural network
    • Constructing a multilayer neural network
    • Building a vector quantizer
    • Analyzing sequential data using recurrent neural networks
    • Visualizing characters in an Optical Character Recognition database
    • Building an Optical Character Recognition engine
    • Summary
  • 15. Reinforcement Learning
    • Understanding the premise
    • Reinforcement learning versus supervised learning
    • Real world examples of reinforcement learning
    • Building blocks of reinforcement learning
    • Creating an environment
    • Building a learning agent
    • Summary
  • 16. Deep Learning with Convolutional Neural Networks
    • What are Convolutional Neural Networks?
    • Architecture of CNNs
    • Types of layers in a CNN
    • Building a perceptron-based linear regressor
    • Building an image classifier using a single layer neural network
    • Building an image classifier using a Convolutional Neural Network
    • Summary

pokaż cały spis treści

ukryj recenzje