https://github.com/codeasarjun/_easy_machine_learning

This repo contains a comprehensive tutorial on machine learning with practical implementations and examples using Python.
https://github.com/codeasarjun/_easy_machine_learning

artificial-intelligence clustering deep-learning jupyter-notebook machine-learning machine-learning-algorithms modelevaluation neural-network numpy pandas preprocessing python python3 regression scipy supervised-learning supervised-machine-learning unsupervised-learning unsupervised-machine-learning

Last synced: 13 days ago
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This repo contains a comprehensive tutorial on machine learning with practical implementations and examples using Python.

• Host: GitHub
• URL: https://github.com/codeasarjun/_easy_machine_learning
• Owner: codeasarjun
• Created: 2024-08-20T03:10:56.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2025-01-14T04:56:26.000Z (15 days ago)
• Last Synced: 2025-01-14T05:37:05.679Z (15 days ago)
• Topics: artificial-intelligence, clustering, deep-learning, jupyter-notebook, machine-learning, machine-learning-algorithms, modelevaluation, neural-network, numpy, pandas, preprocessing, python, python3, regression, scipy, supervised-learning, supervised-machine-learning, unsupervised-learning, unsupervised-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 647 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
This repo is organized into various folders, each covering different aspects of machine learning. 

Inside each folder, you'll find code examples and explanations to help you understand and practice each topic.

  

    

      Folder

      Description

    

  

  

    

      01-python-for-ml

      Basic Python libraries for machine learning.

    

    

        Numpy

      Introduction to Numpy basics.

    

    

        Pandas

      Introduction to Pandas basics.

    

    

        Matplotlib

      Introduction to Matplotlib basics.

    

    

        SciPy

      Introduction to Scipy basics.

    

    

      02-data-preprocessing

      Techniques and methods for preprocessing data before applying machine learning algorithms.

    

    

        Data Preprocessing

      Notebook on data preprocessing techniques.

    

    

      03-exploratory-data-analysis

      Exploratory Data Analysis (EDA) techniques to understand and visualize data.

    

    

        EDA

      Notebook on exploratory data analysis.

    

    

      04-supervised-learning

      Supervised learning methods including classification and regression techniques.

    

    

        Classification

      Notebook on classification techniques.

    

    

        Regression

      Notebook on regression techniques.

    

    

      05-unsupervised-learning

      Unsupervised learning techniques including clustering and dimensionality reduction methods.

    

    

        Clustering

      Notebook on clustering techniques.

    

    

        Dimensionality Reduction

      Notebook on dimensionality reduction techniques.

    

    

      06-model-evaluation

      Techniques for evaluating the performance of machine learning models.

    

    

        Model Evaluation

      Notebook on model evaluation techniques.