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Artificial Intelligence with Python电子书

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124人正在读 | 0人评论 6.2

作       者:Prateek Joshi

出  版  社:Packt Publishing

出版时间:2017-01-01

字       数:56.8万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you’ll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that’s based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
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Artificial Intelligence with Python

Artificial Intelligence with Python

Credits

About the Author

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Introduction to Artificial Intelligence

What is Artificial Intelligence?

Why do we need to study AI?

Applications of AI

Branches of AI

Defining intelligence using 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

2. Classification and Regression Using Supervised Learning

Supervised versus unsupervised learning

What is classification?

Preprocessing data

Binarization

Mean removal

Scaling

Normalization

Label encoding

Logistic Regression classifier

Naïve Bayes classifier

Confusion matrix

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

3. Predictive Analytics with Ensemble Learning

What is Ensemble Learning?

Building learning models with Ensemble Learning

What are Decision Trees?

Building a Decision Tree classifier

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 Extremely Random Forest regressor

Summary

4. Detecting Patterns with Unsupervised Learning

What is unsupervised learning?

Clustering data with K-Means algorithm

Estimating the number of clusters with 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 Affinity Propagation model

Segmenting the market based on shopping patterns

Summary

5. Building Recommender Systems

Creating a training pipeline

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

6. 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

7. Heuristic Search Techniques

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

8. Genetic Algorithms

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

Summary

9. Building Games With Artificial Intelligence

Using search algorithms in games

Combinatorial search

Minimax algorithm

Alpha-Beta pruning

Negamax algorithm

Installing 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

10. 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 a Bag of Words model

Building a category predictor

Constructing a gender identifier

Building a sentiment analyzer

Topic modeling using Latent Dirichlet Allocation

Summary

11. Probabilistic Reasoning for Sequential Data

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

12. 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

13. Object Detection and Tracking

Installing OpenCV

Frame differencing

Tracking objects using colorspaces

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

14. Artificial Neural Networks

Introduction to artificial neural networks

Building a neural network

Training a neural network

Building a Perceptron based classifier

Constructing a single layer neural network

Constructing a multilayer 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

15. Reinforcement Learning

Understanding the premise

Reinforcement learning versus supervised learning

Real world examples of reinforcement learning

Building blocks of reinforcement learning

Creating an environment

Building a learning agent

Summary

16. Deep Learning with Convolutional Neural Networks

What are 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

Summary

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