Artificial Intelligence with Python

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

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About the Author

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Preface

Free Chapter

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

Building an intelligent agent

Installing Python 3

Installing packages

Loading data

Summary

Classification and Regression Using Supervised Learning

Supervised versus unsupervised learning

What is classification?

Preprocessing data

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

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

Summary

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

Building Recommender Systems

Creating a training pipeline

Extracting the nearest neighbors

Building a K-Nearest Neighbors classifier

Computing similarity scores

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

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

Summary

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

Building Games With Artificial Intelligence

Using search algorithms in games

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

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

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

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

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

Summary

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

Summary

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

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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Extracting speech features

We learnt how to convert a time domain signal into the frequency domain. Frequency domain features are used extensively in all the speech recognition systems. The concept we discussed earlier is an introduction to the idea, but real world frequency domain features are a bit more complex. Once we convert a signal into the frequency domain, we need to ensure that it's usable in the form of a feature vector. This is where the concept of Mel Frequency Cepstral Coefficients (MFCCs) becomes relevant. MFCC is a tool that's used to extract frequency domain features from a given audio signal.

In order to extract the frequency features from an audio signal, MFCC first extracts the power spectrum. It then uses filter banks and a discrete cosine transform (DCT) to extract the features. If you are interested in exploring MFCC further, you can check out this link: http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs...

Artificial Intelligence with Python

Artificial Intelligence with Python

Overview of this book

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