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

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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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Applications of AI

Now that we know how information gets processed, let's see where AI appears in the real world. AI manifests itself in various different forms across multiple fields, so it's important to understand how it's useful in various domains. AI has been used across many industries and it continues to expand rapidly. Some of the most popular areas include:

Computer Vision: These are the systems that deal with visual data such as images and videos. These systems understand the content and extract insights based on the use case. For example, Google uses reverse image search to search for visually similar images across the Web.

Natural Language Processing: This field deals with understanding text. We can interact with a machine by typing natural language sentences. Search engines use this extensively to deliver the right search results.
Speech Recognition: These systems are capable of hearing and understanding spoken words. For example, there are intelligent personal assistants on our smartphones that can understand what we are saying and give relevant information or perform an action based on that.
Expert Systems: These systems use AI techniques to provide advice or make decisions. They usually use databases of expert knowledge areas such as finance, medicine, marketing, and so on to give advice about what to do next. Let's see what an expert system looks like and how it interacts with the user:

Games: AI is used extensively in the gaming industry. It is used to design intelligent agents that can compete with humans. For example, AlphaGo is a computer program that can play the strategy game Go. It is also used in designing many other types of games where we expect the computer to behave intelligently.
Robotics: Robotic systems actually combine many concepts in AI. These systems are able to perform many different tasks. Depending on the situation, robots have sensors and actuators that can do different things. These sensors can see things in front of them and measure the temperature, heat, movements, and so on. They have processors on board that compute various things in real time. They are also capable of adapting to the new environments.