book

Artificial Intelligence with Python

Name: Artificial Intelligence with Python
Author: Prateek Joshi
ISBN: 9781786464392

by Prateek Joshi

January 2017

Beginner to intermediate

446 pages

8h 46m

English

Packt Publishing

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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 UbuntuInstalling on Mac OS XInstalling on Windows
Installing packages
Loading data
Summary
2. Classification and Regression Using Supervised Learning
Supervised versus unsupervised learning
What is classification?
Preprocessing data
BinarizationMean removalScalingNormalization
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 classifiersEstimating 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 detectionUsing integral images for feature extraction
Eye detection and tracking
Summary
14. Artificial Neural Networks
Introduction to artificial neural networksBuilding a neural networkTraining 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

Content preview from Artificial Intelligence with Python

Solving a problem with constraints

We have already discussed how Constraint Satisfaction Problems are formulated. Let's apply them to a real-world problem. In this problem, we have a list of names and each name can only take a fixed set of values. We also have a set of constraints between these people that needs to be satisfied. Let's see how to do it.

Create a new Python file and import the following packages:

from simpleai.search import CspProblem, backtrack, \ 
    min_conflicts, MOST_CONSTRAINED_VARIABLE, \ 
    HIGHEST_DEGREE_VARIABLE, LEAST_CONSTRAINING_VALUE

Define the constraint that specifies that all the variables in the input list should have unique values:

# Constraint that expects all the different variables # to have different values def constraint_unique(variables, ...

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Artificial Intelligence with Python - Second Edition

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ISBN: 9781786464392

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Artificial Intelligence with Python

by Prateek Joshi

Solving a problem with constraints

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

More than 5,000 organizations count on O’Reilly

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