Artificial Intelligence with Python

Book Image

Artificial Intelligence with Python

Book Image

Artificial Intelligence with Python

Overview of this book

Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps which intelligently interact with the world around you, building intelligent recommender systems, automatic speech recognition systems and more. Starting with AI basics you'll move on to learn how to develop building blocks using data mining techniques. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. This practical book covers a range of topics including predictive analytics and deep learning.

Artificial Intelligence with Python

Artificial Intelligence with Python

Credits

About the Author

About the Author

About the Reviewer

About the Reviewer

www.PacktPub.com

www.PacktPub.com

Customer Feedback

Customer Feedback

Preface

Free Chapter

Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

What is Artificial Intelligence?

Why do we need to study AI?

Applications of AI

Defining intelligence using Turing Test

Making machines think like humans

Building rational agents

General Problem Solver

Building an intelligent agent

Installing Python 3

Installing packages

Classification and Regression Using Supervised Learning

Classification and Regression Using Supervised Learning

Supervised versus unsupervised learning

What is classification?

Preprocessing data

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

Predictive Analytics with Ensemble Learning

Predictive Analytics with Ensemble Learning

What is Ensemble Learning?

What are Decision Trees?

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 Extremely Random Forest regressor

Detecting Patterns with Unsupervised Learning

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

Building Recommender Systems

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

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

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

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

Building Games With Artificial Intelligence

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

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 a Bag of Words model

Building a category predictor

Constructing a gender identifier

Building a sentiment analyzer

Topic modeling using Latent Dirichlet Allocation

Probabilistic Reasoning for Sequential Data

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

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

Object Detection and Tracking

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

Eye detection and tracking

Artificial Neural Networks

Artificial Neural Networks

Introduction to artificial neural networks

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

Reinforcement Learning

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

Deep Learning with Convolutional Neural Networks

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

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Summary

In this chapter, we learnt about reinforcement learning systems. We discussed the premise of reinforcement learning and how we can set it up. We talked about the differences between reinforcement learning and supervised learning. We went through some real world examples of reinforcement learning and saw how various systems use it in different forms.

We discussed the building blocks of reinforcement learning and concepts such as agent, environment, policy, reward, and so on. We then created an environment in python to see it in action. We used these concepts to build a reinforcement learning agent.