AI Fundamentals

Welcome to the AI Fundamentals repository! This repository contains example code and exercises for understanding basic concepts of AI and Machine Learning using Python.

Table of Contents

• Introduction
• Prerequisites
• Installation
• Usage
• Exercises
  • Python Basics
  • Data Manipulation with Pandas
  • Linear Regression
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM)
  • K-Means Clustering
  • Computer Vision
  • Natural Language Processing
• Contributing
• License

Introduction

This repository aims to provide a foundational understanding of AI and Machine Learning concepts through practical exercises and example code. The materials are designed to help beginners get started with AI and ML using Python.

Prerequisites

• Basic knowledge of Python programming
• VS Code installed with Python and Jupyter extension.
• Some familiarity with data science libraries like NumPy, Pandas, and Scikit-learn

Installation and Usage

1. Clone the repository:

   git clone https://github.com/myduino/AI-Fundamentals.git

2. Navigate to the project directory:

   cd AI-Fundamentals

3. Open the project directory in Visual Studio Code:

   code .

Exercises

1. Python Basics

Basic Python programming concepts and syntax.

2. Data Manipulation with Pandas

Introduction to data manipulation and analysis using the Pandas library.

3. Linear Regression

Implementing and understanding linear regression models.

4. K-Nearest Neighbors (KNN)

Exploring the KNN algorithm for classification problems.

5. Support Vector Machines (SVM)

Understanding and implementing SVM for classification tasks.

6. K-Means Clustering

Applying K-Means clustering for unsupervised learning.

7. Computer Vision

Exercises on face detection and recognition using computer vision techniques.

8. Natural Language Processing

Performing sentiment analysis using NLP techniques.

License

This project is licensed under the MIT License - see the LICENSE file for details.