Machine Learning Basics with Python

Welcome to the Machine Learning Basics repository! This project is a comprehensive guide to foundational ML concepts, along with Python implementations for each topic. Whether you're a beginner or revisiting the basics, you'll find this repository helpful.

---

Table of Contents

1. Data Handling
   • Data Loading
   • Data Cleaning
   • Feature Engineering
2. Supervised Learning
   • K-Nearest Neighbors (KNN)
   • Decision Trees
   • Random Forests
   • Support Vector Machines (SVM)
   • Logistic Regression
3. Unsupervised Learning
   • K-Means Clustering
   • Principal Component Analysis (PCA)
4. Regression Techniques
   • Linear Regression
   • Polynomial Regression
5. Advanced Models
   • XGBoost
   • Deep Learning (ANN, CNN)

---

Concepts and Examples

1. Data Handling

• Data Loading: Learn to import datasets from CSV, Excel, or APIs using libraries like pandas.
• Data Cleaning: Techniques to handle missing values, duplicates, and outliers.
• Feature Engineering: Preprocessing steps like normalization, encoding categorical features, and scaling.

2. Supervised Learning

• K-Nearest Neighbors (KNN): A simple algorithm that classifies data points based on their proximity to neighbors.
• Decision Trees: Visual tree-based models for classification and regression tasks.
• Random Forests: An ensemble method combining multiple decision trees for improved accuracy.
• SVM: A robust classifier that separates data with hyperplanes.
• Logistic Regression: Predict probabilities and classify data into binary categories.

3. Unsupervised Learning

• K-Means Clustering: Group similar data points into clusters based on their features.
• PCA: Reduce dimensionality while preserving maximum variance.

4. Regression Techniques

• Linear Regression: Predict continuous variables based on input features.
• Polynomial Regression: Capture non-linear relationships using polynomial terms.

5. Advanced Models

• XGBoost: A powerful gradient boosting algorithm for both classification and regression.
• Deep Learning: Implement Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) to handle complex datasets.

---

Running the Examples

1. Clone the repository:

   git clone <repo_url>

2. Install dependencies:

   pip install <the_dependencies>

3. Run example code

   python <file_name>.py

This repository simplifies ML concepts and demonstrates practical python implementations, making it easy for beginners to grasp and apply various concepts.