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Table of Contents
- Cover
- Copyright
- Packt Page
- Contributors
- Table of Contents
- Preface
- Chapter 1: 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
- Chapter 2: 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
- Chapter 3: 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
- Chapter 4: 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
- Feature selection
- Chapter 5: 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 Naïve 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
- Chapter 6: 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
- What are decision trees?
- Chapter 7: 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
- Chapter 8: 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
- Chapter 9: 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 10: 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
- Chapter 11: 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
- Chapter 12: 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
- Chapter 13: 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
- Chapter 14: 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
- Chapter 15: 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
- Chapter 16: 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
- Chapter 17: 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
- Chapter 18: 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
- Chapter 19: 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
- Introduction to neural networks
- Chapter 20: 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
- Chapter 21: 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 aanguage modeling use case
- Training an RNN
- Summary
- The basics of Recurrent Neural Networks
- Chapter 22: 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
- Chapter 23: Artificial Intelligence and Big Data
- Big data basics
- Crawling
- Indexing
- Ranking
- Worldwide datacenters
- Distributed lookups
- Custom software
- The three V's 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
- Apache Hadoop
- NoSQL Databases
- Types of NoSQL databases
- Apache Cassandra
- MongoDB
- Redis
- Neo4j
- Summary
- Big data basics
- Other Books You May Enjoy
- Index
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