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The official code repository supporting the book, Grokking Artificial Intelligence Algorithms

Home Page: https://bit.ly/gaia-manning

License: GNU Affero General Public License v3.0

Python 100.00%
python algorithm ai machine-learning-algorithms machine-learning search-algorithm evolutionary-algorithms genetic-algorithm particle-swarm-optimization ant-colony-optimization

grokking-artificial-intelligence-algorithms's Introduction

Grokking Artificial Intelligence Algorithms

Get Grokking Artificial Intelligence Algorithms at Manning Publications

Rather Learn by exploring the code notebook in your browser? Click here:

Open the interactive code notebook

Requirements

  • Python 3.7.0+
  • Pip 3

Setup

Make sure that you have Python 3.7.0+ installed. Download Python here. pip3 should be installed with Python 3.7.0+ on macOS and Windows. You may need to install pip3 seperately if you're using Linux. Use the sudo apt-get install python3-pip command if you're using Ubuntu or Debian Linux. Use the sudo yum install python3-pip command if you're using Fedora Linux.

Clone this repository.

Navigate to the directory that contains the repository using terminal/console/command line.

Run this command to install the dependency libraries required.

pip3 install -r requirements.txt

Navigate to the directory containing the example that you want to run, and run this command.

python3 <file_name.py>

Overview

This is the official supporting code for the book, Grokking Artificial Intelligence Algorithms, published by Manning Publications, authored by Rishal Hurbans.

History of AI

The example implementations provided will make more sense if you've read the book, however, it might be somewhat useful to you otherwise.

The purpose of this repository is to act as a practical reference for examples of how the algorithms mentioned in the book can be implemented. This repository should not be consulted as the book is read page-by-page, but rather, when you're attempting to implement an algorithm or gain a more technical understanding from a programming perspective.

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grokking-artificial-intelligence-algorithms's Issues

Issue in ml_scikitlearn_decision_tree_classification_simple.py

Getting following error while executing ml_scikitlearn_decision_tree_classification_simple.py :

Traceback (most recent call last):
File "ml_scikitlearn_decision_tree_classification_simple.py", line 54, in
graph.write_png('tree.png')
File "C:\Installers\Python\Python37\lib\site-packages\pydotplus\graphviz.py", line 1810, in
prog=self.prog: self.write(path, format=f, prog=prog)
File "C:\Installers\Python\Python37\lib\site-packages\pydotplus\graphviz.py", line 1918, in write
fobj.write(self.create(prog, format))
File "C:\Installers\Python\Python37\lib\site-packages\pydotplus\graphviz.py", line 1960, in create
'GraphViz's executables not found')
pydotplus.graphviz.InvocationException: GraphViz's executables not found

ml_scikitlearn_linear_regression code

Hello Rishal Hurbans,

Firstly, congratulations for your book, it is awesome.

I have a question regarding ml_scikitlearn_linear_regression code. x is usually used for the columns that we use for prediction and y for the labels we want to predict. However, in the ml_scikitlearn_linear_regression code, reg_test_x is used for price, and reg_test_y is used for carat (lines 26 and 27). Is it the other way around? Maybe I am misunderstanding something.

Best,
hsang

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