Artificial Intelligence with Python E-Book

Alberto Artasanchez

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 723

Veröffentlichungsjahr: 2020

Beschreibung

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.

Key Features

• Completely updated and revised to Python 3.x
  
  


• New chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineering
  
  


• Learn more about deep learning algorithms, machine learning data pipelines, and chatbots

Book Description

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.

What you will learn

• Understand what artificial intelligence, machine learning, and data science are
  
  


• Explore the most common artificial intelligence use cases
  
  


• Learn how to build a machine learning pipeline
  
  


• Assimilate the basics of feature selection and feature engineering
  
  


• Identify the differences between supervised and unsupervised learning
  
  


• Discover the most recent advances and tools offered for AI development in the cloud
  
  


• Develop automatic speech recognition systems and chatbots
  
  


• Apply AI algorithms to time series data

Who this book is for

The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

Leseprobe

Artificial Intelligence with Python

Second Edition

Your complete guide to building intelligent apps using Python 3.x

Alberto Artasanchez

Prateek Joshi

BIRMINGHAM - MUMBAI

Artificial Intelligence with Python

Second Edition

Copyright © 2020 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Producer: Tushar Gupta

Acquisition Editor – Peer Reviews: Suresh Jain

Content Development Editor: Ian Hough

Technical Editor: Aniket Shetty

Project Editor: Carol Lewis

Proofreader: Safis Editing

Indexer: Tejal Daruwale Soni

Presentation Designer: Sandip Tadge

First published: January 2017

Second edition: January 2020

Production reference: 2030220

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-83921-953-5

www.packt.com

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Contributors

About the authors

Alberto Artasanchez is a data scientist with over 25 years of consulting experience with Fortune 500 companies as well as startups. He has an extensive background in artificial intelligence and advanced algorithms. Mr. Artasanchez holds 9 AWS certifications including the Big Data Specialty and the Machine Learning Specialty certifications. He is an AWS Ambassador and publishes frequently in a variety of data science blogs. He is often tapped as a speaker on topics ranging from Data Science, Big Data, and Analytics to underwriting optimization and fraud detection. He has a strong and extensive track record designing and building end-to-end machine learning platforms at scale.

He graduated with a Master of Science degree from Wayne State University and a Bachelor of Arts degree from Kalamazoo College. He is particularly interested in using Artificial Intelligence to build Data Lakes at scale. He is married to his lovely wife Karen and is addicted to CrossFit.

I would like to thank the wonderful editors at Packt for all the help. This book would have never been completed without their invaluable assistance. They are Ian Hough, Carol Lewis, Ajinkya Kolhe, Aniket Shetty, and Tushar Gupta. I would also like to thank my amazing wife Karen Artasanchez for her support and patience, not just with this book but for her continued support in all my crazy endeavors. I would like to dedicate this book to my father Alberto Artasanchez Madrigal and my mother Laura Loy Loy.

Prateek Joshi is the founder of Plutoshift and a published author of 9 books on artificial intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. His tech blog (www.prateekjoshi.com) has received more than 2M page views from 200+ countries and has 7,500+ followers. You can learn more about him on www.prateekj.com. Apart from artificial intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make artificial intelligence accessible to everyone so that it can impact billions of people around the world.

About the reviewer

Ajinkya Kolhe is a Data Analytics and Machine Learning Instructor. He started his journey as a software developer at Morgan Stanley and transitioned into the machine learning field. He is now working as an instructor to help companies and individuals to get started with Artificial Intelligence.

Contents

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 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

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 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

Other Books You May Enjoy

Index

Landmarks

Cover

Index