Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi

Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.

Preface

Who this book is for

What this book covers

What you need for this book

Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

Why do we need to study AI?

The five tribes of machine learning

Defining intelligence using the Turing test

Making machines think like humans

Building rational agents

General Problem Solver

Building an intelligent agent

Installing Python 3

Installing packages

Free Chapter

Fundamental Use Cases for Artificial Intelligence

Fundamental Use Cases for Artificial Intelligence

Representative AI use cases

Digital personal assistants and chatbots

Personal chauffeur

Shipping and warehouse management

Knowledge search

Recommendation systems

Underwriting and deal analysis

Data cleansing and transformation

Machine Learning Pipelines

Machine Learning Pipelines

What is a machine learning pipeline?

Problem definition

Data preparation

Data segregation

Feature Selection and Feature Engineering

Feature Selection and Feature Engineering

Feature selection

Feature engineering

Outlier management

One-hot encoding

Date manipulation

Classification and Regression Using Supervised Learning

Classification and Regression Using Supervised Learning

Supervised versus unsupervised learning

What is classification?

Preprocessing data

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

Predictive Analytics with Ensemble Learning

Predictive Analytics with Ensemble Learning

What are decision trees?

What is ensemble learning?

What are random forests and extremely random forests?

Dealing with class imbalance

Finding optimal training parameters using grid search

Computing relative feature importance

Predicting traffic using an extremely random forest regressor

Detecting Patterns with Unsupervised Learning

Detecting Patterns with Unsupervised Learning

What is unsupervised learning?

Clustering data with the K-Means algorithm

What are Gaussian Mixture Models?

Finding subgroups in stock market using the Affinity Propagation model

Segmenting the market based on shopping patterns

Building Recommender Systems

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

Logic Programming

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

Heuristic Search Techniques

Heuristic Search Techniques

Is heuristic search artificial intelligence?

What is heuristic search?

Constraint satisfaction problems

Local search techniques

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

Genetic Algorithms and Genetic Programming

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

Artificial Intelligence on the Cloud

Artificial Intelligence on the Cloud

Why are companies migrating to the cloud?

The top cloud providers

Amazon Web Services (AWS)

Microsoft Azure

Google Cloud Platform (GCP)

Building Games with Artificial Intelligence

Building Games with Artificial Intelligence

Using search algorithms in games

Combinatorial search

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

Building a Speech Recognizer

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

Natural Language Processing

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

Chatbots

The future of chatbots

Chatbot concepts

A well-architected chatbot

Chatbot platforms

Creating a chatbot using DialogFlow

Sequential Data and Time Series Analysis

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

Image Recognition

Image Recognition

Importance of image recognition

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

Eye detection and tracking

Neural Networks

Neural Networks

Introduction to neural networks

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

Deep Learning with Convolutional Neural Networks

Deep Learning with Convolutional Neural Networks

The basics of 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

Recurrent Neural Networks and Other Deep Learning Models

Recurrent Neural Networks and Other Deep Learning Models

The basics of Recurrent Neural Networks

Architecture of RNNs

A language modeling use case

Training an RNN

Creating Intelligent Agents with Reinforcement Learning

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

Artificial Intelligence and Big Data

Artificial Intelligence and Big Data

Big data basics

The three V's of big data

Big data and machine learning

NoSQL Databases

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Computing similarity scores

To build a recommendation system, it is important to understand how to compare various objects in the dataset. If the dataset consists of people and their various movie preferences, then in order to make a recommendation we need to understand how to compare any two people with one another. This is where the similarity score is important. The similarity score gives an idea of how similar two data points are.

There are two scores that are used frequently in this domain – the Euclidean score and the Pearson score. The Euclidean score uses the Euclidean distance between two data points to compute the score. If you need a quick refresher on how Euclidean distance is computed, you can go to:

https://en.wikipedia.org/wiki/Euclidean_distance

The value of the Euclidean distance can be unbounded. Hence, we take this value and convert it in a way that the Euclidean score ranges from 0 to 1. If the Euclidean distance between two objects is large...