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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Index