Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x and TensorFlow 2

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
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
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
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
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
	NoSQL Databases
		Types of NoSQL databases
		Apache Cassandra
		MongoDB
		Redis
		Neo4j
	Summary
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