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AI-ML-Jupyter-Notebooks Learning Guide

Welcome to the AI-ML-Jupyter-Notebooks repository! This guide will help you navigate and learn from the collection of Jupyter notebooks available here. Whether you're a beginner or an experienced practitioner in the field of Artificial Intelligence (AI) and Machine Learning (ML), you'll find valuable resources to enhance your knowledge and skills.

Table of Contents

  1. Getting Started
  2. Machine Learning
  3. Deep Learning
  4. Data Preprocessing
  5. Data Visualization
  6. Natural Language Processing
  7. Computer Vision
  8. Reinforcement Learning
  9. Additional Topics
  10. Contributing
  11. License

Getting Started

  • Introduction to AI and ML: Start here if you're new to AI and ML. This notebook provides an overview of key concepts and terminology.

Machine Learning

  • Linear Regression: Learn about linear regression and its applications.
  • Decision Trees: Dive into decision trees and their use in classification tasks.
  • [...]: Explore more machine learning algorithms.

Deep Learning

Data Preprocessing

  • Data Cleaning: Learn techniques to clean and preprocess datasets with an example dataset. All Steps are Visualised and Explained in Detail.
  • Feature Selection: Understand how to select relevant features for your models.
  • [...]: Explore data preprocessing best practices.

Data Visualization

  • Matplotlib Basics: Get started with data visualization using Matplotlib.
  • Introduction to Seaborn: Explore Seaborn for more advanced data visualization.
  • [...]: Enhance your data visualization skills.

Natural Language Processing

Computer Vision

Reinforcement Learning

Additional Topics

Contributing

We welcome contributions from the community! If you have your own Jupyter notebooks, tutorials, or improvements to existing content, please follow our contributing guidelines to get started.

License

This project is open-source and available under the [License Name] License. Please review the licensing terms before using or contributing to this repository.


Happy learning and contributing! 🚀