Artificial Intelligence with Python - Second Edition

Spis treści książki

• Preface
  • Who this book is for
  • What this book covers
  • What you need for this book
    • Download the example code files
    • Download the color images
    • Conventions used
  • Get in touch
    • Reviews
• Introduction to Artificial Intelligence
  • What is AI?
  • Why do we need to study AI?
  • Branches of AI
  • The five tribes of machine learning
  • Defining intelligence using the 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
• Fundamental Use Cases for Artificial Intelligence
  • Representative AI use cases
  • Digital personal assistants and chatbots
  • Personal chauffeur
  • Shipping and warehouse management
  • Human health
  • Knowledge search
  • Recommendation systems
  • The smart home
  • Gaming
  • Movie making
  • Underwriting and deal analysis
  • Data cleansing and transformation
  • Summary
  • References
• Machine Learning Pipelines
  • What is a machine learning pipeline?
  • Problem definition
  • Data ingestion
  • Data preparation
    • Missing values
    • Duplicate records or values
    • Feature scaling
    • Inconsistent values
    • Inconsistent date formatting
  • Data segregation
  • Model training
    • Candidate model evaluation and selection
    • Model deployment
    • Performance monitoring
      • Model performance
      • Operational performance
      • Total cost of ownership (TCO)
      • Service performance
  • Summary
• Feature Selection and Feature Engineering
  • Feature selection
    • Feature importance
    • Univariate selection
    • Correlation heatmaps
      • Wrapper-based methods
      • Filter-based methods
      • Embedded methods
  • Feature engineering
    • Imputation
  • Outlier management
  • One-hot encoding
  • Log transform
  • Scaling
  • Date manipulation
  • Summary
• Classification and Regression Using Supervised Learning
  • Supervised versus unsupervised learning
  • What is classification?
  • Preprocessing data
    • Binarization
    • Mean removal
    • Scaling
    • Normalization
  • Label encoding
  • Logistic regression classifiers
  • The Nave Bayes classifier
  • Confusion matrixes
  • 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
• Predictive Analytics with Ensemble Learning
  • What are decision trees?
    • Building a decision tree classifier
  • What is ensemble learning?
    • Building learning models with ensemble learning
  • 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 an extremely random forest regressor
  • Summary
• Detecting Patterns with Unsupervised Learning
  • What is unsupervised learning?
  • Clustering data with the K-Means algorithm
    • Estimating the number of clusters with the 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 the Affinity Propagation model
  • Segmenting the market based on shopping patterns
  • Summary
• Building Recommender Systems
  • 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
• 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
• Heuristic Search Techniques
  • Is heuristic search artificial intelligence?
  • 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
• Genetic Algorithms and Genetic Programming
  • The evolutionists tribe
  • 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
  • Genetic programming use cases
  • Summary
  • References
• Artificial Intelligence on the Cloud
  • Why are companies migrating to the cloud?
  • The top cloud providers
  • Amazon Web Services (AWS)
    • Amazon SageMaker
    • Alexa, Lex, and Polly conversational gents
    • Amazon Comprehend natural language processing
    • Amazon Rekognition image and video
    • Amazon Translate
    • Amazon machine learning
    • Amazon Transcribe transcription
    • Amazon Textract document analysis
  • Microsoft Azure
    • Microsoft Azure Machine Learning Studio
    • Azure Machine Learning Service
    • Azure Cognitive Services
  • Google Cloud Platform (GCP)
    • AI Hub
    • Google Cloud AI Building Blocks
  • Summary
• Building Games with Artificial Intelligence
  • Using search algorithms in games
  • Combinatorial search
    • The Minimax algorithm
    • Alpha-Beta pruning
    • The Negamax algorithm
  • Installing the 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
• 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
• 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 the Bag of Words model
  • Building a category predictor
  • Constructing a gender identifier
  • Building a sentiment analyzer
  • Topic modeling using Latent Dirichlet Allocation
  • Summary
• Chatbots
  • The future of chatbots
  • Chatbots today
  • Chatbot concepts
  • A well-architected chatbot
  • Chatbot platforms
  • Creating a chatbot using DialogFlow
    • DialogFlow setup
    • Integrating a chatbot into a website using a widget
    • Integrating a chatbot into a website using Python
    • How to set up a webhook in DialogFlow
    • Enabling webhooks for intents
    • Setting up training phrases for an intent
    • Setting up parameters and actions for an intent
    • Building fulfillment responses from a webhook
    • Checking responses from a webhook
  • Summary
• Sequential Data and Time Series Analysis
  • 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
• Image Recognition
  • Importance of image recognition
  • OpenCV
  • Frame differencing
  • Tracking objects using color spaces
  • 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
• Neural Networks
  • Introduction to neural networks
    • Building a neural network
    • Training a neural network
  • Building a Perceptron-based classifier
  • Constructing a single-layer neural network
  • Constructing a multi-layer 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
• Deep Learning with Convolutional Neural Networks
  • The basics of Convolutional Neural Networks
  • Architecture of CNNs
    • CNNs vs. perceptron neural networks
  • 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
  • Reference
• Recurrent Neural Networks and Other Deep Learning Models
  • The basics of Recurrent Neural Networks
    • Step function
    • Sigmoid function
    • Tanh function
    • ReLU function
  • Architecture of RNNs
  • A language modeling use case
  • Training an RNN
  • Summary
• Creating Intelligent Agents with Reinforcement Learning
  • Understanding what it means to learn
  • Reinforcement learning versus supervised learning
  • Real-world examples of reinforcement learning
  • Building blocks of reinforcement learning
  • Creating an environment
  • Building a learning agent
  • Summary
• Artificial Intelligence and Big Data
  • Big data basics
    • Crawling
    • Indexing
    • Ranking
    • Worldwide datacenters
    • Distributed lookups
    • Custom software
  • The three Vs of big data
    • Volume
    • Velocity
    • Variety
  • Big data and machine learning
    • Apache Hadoop
      • MapReduce
      • Apache Hive
    • Apache Spark
      • Resilient distributed datasets
      • DataFrames
      • SparkSQL
    • Apache Impala
  • NoSQL Databases
    • Types of NoSQL databases
    • Apache Cassandra
    • MongoDB
    • Redis
    • Neo4j
  • Summary
• Other Books You May Enjoy
• Index

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