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  • Book Overview & Buying Artificial Intelligence with Python
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Artificial Intelligence with Python

Artificial Intelligence with Python

By : Prateek Joshi
3.6 (34)
Buy this Book
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Artificial Intelligence with Python

Artificial Intelligence with Python

3.6 (34)
By: Prateek Joshi
Buy this Book

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.
Table of Contents (17 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to Artificial Intelligence
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1. Introduction to Artificial Intelligence
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What is Artificial Intelligence?
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Why do we need to study AI?
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Applications of AI
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Branches of AI
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Defining intelligence using Turing Test
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Making machines think like humans
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Building rational agents
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General Problem Solver
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Building an intelligent agent
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Installing Python 3
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Installing packages
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Loading data
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Summary
2
2. Classification and Regression Using Supervised Learning
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2. Classification and Regression Using Supervised Learning
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Supervised versus unsupervised learning
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What is classification?
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Preprocessing data
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Label encoding
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Logistic Regression classifier
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Naïve Bayes classifier
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Confusion matrix
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Support Vector Machines
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Classifying income data using Support Vector Machines
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What is Regression?
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Building a single variable regressor
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Building a multivariable regressor
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Estimating housing prices using a Support Vector Regressor
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Summary
3
3. Predictive Analytics with Ensemble Learning
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3. Predictive Analytics with Ensemble Learning
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What is Ensemble Learning?
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What are Decision Trees?
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What are Random Forests and Extremely Random Forests?
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Dealing with class imbalance
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Finding optimal training parameters using grid search
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Computing relative feature importance
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Predicting traffic using Extremely Random Forest regressor
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Summary
4
4. Detecting Patterns with Unsupervised Learning
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4. Detecting Patterns with Unsupervised Learning
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What is unsupervised learning?
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Clustering data with K-Means algorithm
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Estimating the number of clusters with Mean Shift algorithm
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Estimating the quality of clustering with silhouette scores
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What are Gaussian Mixture Models?
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Building a classifier based on Gaussian Mixture Models
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Finding subgroups in stock market using Affinity Propagation model
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Segmenting the market based on shopping patterns
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Summary
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5. Building Recommender Systems
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5. Building Recommender Systems
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Creating a training pipeline
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Extracting the nearest neighbors
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Building a K-Nearest Neighbors classifier
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Computing similarity scores
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Finding similar users using collaborative filtering
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Building a movie recommendation system
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Summary
6
6. Logic Programming
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6. Logic Programming
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What is logic programming?
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Understanding the building blocks of logic programming
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Solving problems using logic programming
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Installing Python packages
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Matching mathematical expressions
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Validating primes
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Parsing a family tree
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Analyzing geography
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Building a puzzle solver
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Summary
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7. Heuristic Search Techniques
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7. Heuristic Search Techniques
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What is heuristic search?
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Constraint Satisfaction Problems
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Local search techniques
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Constructing a string using greedy search
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Solving a problem with constraints
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Solving the region-coloring problem
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Building an 8-puzzle solver
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Building a maze solver
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Summary
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8. Genetic Algorithms
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8. Genetic Algorithms
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Understanding evolutionary and genetic algorithms
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Fundamental concepts in genetic algorithms
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Generating a bit pattern with predefined parameters
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Visualizing the evolution
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Solving the symbol regression problem
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Building an intelligent robot controller
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Summary
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9. Building Games With Artificial Intelligence
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9. Building Games With Artificial Intelligence
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Using search algorithms in games
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Combinatorial search
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Minimax algorithm
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Alpha-Beta pruning
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Negamax algorithm
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Installing easyAI library
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Building a bot to play Last Coin Standing
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Building a bot to play Tic-Tac-Toe
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Building two bots to play Connect Four™ against each other
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Building two bots to play Hexapawn against each other
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Summary
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10. Natural Language Processing
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10. Natural Language Processing
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Introduction and installation of packages
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Tokenizing text data
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Converting words to their base forms using stemming
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Converting words to their base forms using lemmatization
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Dividing text data into chunks
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Extracting the frequency of terms using a Bag of Words model
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Building a category predictor
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Constructing a gender identifier
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Building a sentiment analyzer
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Topic modeling using Latent Dirichlet Allocation
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Summary
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11. Probabilistic Reasoning for Sequential Data
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11. Probabilistic Reasoning for Sequential Data
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Understanding sequential data
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Handling time-series data with Pandas
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Slicing time-series data
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Operating on time-series data
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Extracting statistics from time-series data
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Generating data using Hidden Markov Models
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Identifying alphabet sequences with Conditional Random Fields
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Stock market analysis
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Summary
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12. Building A Speech Recognizer
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12. Building A Speech Recognizer
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Working with speech signals
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Visualizing audio signals
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Transforming audio signals to the frequency domain
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Generating audio signals
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Synthesizing tones to generate music
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Extracting speech features
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Recognizing spoken words
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Summary
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13. Object Detection and Tracking
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13. Object Detection and Tracking
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Installing OpenCV
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Frame differencing
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Tracking objects using colorspaces
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Object tracking using background subtraction
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Building an interactive object tracker using the CAMShift algorithm
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Optical flow based tracking
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Face detection and tracking
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Eye detection and tracking
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Summary
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14. Artificial Neural Networks
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14. Artificial Neural Networks
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Introduction to artificial neural networks
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Building a Perceptron based classifier
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Constructing a single layer neural network
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Constructing a multilayer neural network
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Building a vector quantizer
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Analyzing sequential data using recurrent neural networks
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Visualizing characters in an Optical Character Recognition database
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Building an Optical Character Recognition engine
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Summary
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15. Reinforcement Learning
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15. Reinforcement Learning
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Understanding the premise
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Reinforcement learning versus supervised learning
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Real world examples of reinforcement learning
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Building blocks of reinforcement learning
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Creating an environment
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Building a learning agent
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Summary
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16. Deep Learning with Convolutional Neural Networks
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16. Deep Learning with Convolutional Neural Networks
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What are Convolutional Neural Networks?
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Architecture of CNNs
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Types of layers in a CNN
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Building a perceptron-based linear regressor
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Building an image classifier using a single layer neural network
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Building an image classifier using a Convolutional Neural Network
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Summary

Building a bot to play Tic-Tac-Toe

Tic-Tac-Toe (Noughts and Crosses) is probably one of the most famous games. Let's see how to build a game where the computer can play against the user. This is a minor variant of the Tic-Tac-Toe recipe given in the easyAI library.

Create a new Python file and import the following packages:

from easyAI import TwoPlayersGame, AI_Player, Negamax 
from easyAI.Player import Human_Player

Define a class that contains all the methods to play the game. Start by defining the players and who starts the game:

class GameController(TwoPlayersGame): 
    def __init__(self, players): 
        # Define the players 
        self.players = players 
 
        # Define who starts the game 
        self.nplayer = 1

We will be using a 3x3 board numbered from one to nine row-wise:

        # Define the board 
        self.board = [0] * 9

Define a method to compute all the possible moves:

    # Define possible moves 
  ...
CONTINUE READING
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Tech Concepts
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Programming languages
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Artificial Intelligence with Python
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