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.

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Table of Contents (23 chapters)
Artificial Intelligence with Python
Artificial Intelligence with Python
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Introduction to Artificial Intelligence
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
2
Classification and Regression Using Supervised Learning
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
3
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
Summary
4
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
Summary
5
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
Summary
6
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
Summary
7
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
Summary
8
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
Summary
9
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
Summary
10
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
Summary
11
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
Summary
12
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
Summary
13
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
Summary
14
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
Summary
15
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
Summary
16
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
Summary

Fundamental concepts in genetic algorithms

In order to build a genetic algorithm, we need to understand several key concepts and terminology. These concepts are used extensively throughout the field of genetic algorithms to build solutions to various problems. One of the most important aspects of genetic algorithms is the randomness. In order to iterate, it relies on the random sampling of individuals. This means that the process is non-deterministic. So, if you run the same algorithm multiple times, you might end up with different solutions.

Let's talk about population. A population is a set of individuals that are possible candidate solutions. In a genetic algorithm, we do not maintain a single best solution at any given stage. It maintains a set of potential solutions, one of which is the best. But the other solutions play an important role during the search. Since we have a population of solutions, it is less likely that will get stuck in a local optimum. Getting stuck in the local optimum...

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