Skip to content

README.md

Latest commit

 

History

History
155 lines (109 loc) · 2.91 KB

README.md

File metadata and controls

155 lines (109 loc) · 2.91 KB
Raw

Machine Learning Journey (Basic to Advanced)

This repository contains my complete journey of learning Machine Learning, starting from fundamentals to advanced concepts. It includes theory, practical implementations, and real-world projects using Python.

Overview

This repo is a structured collection of:

  • Fundamental ML concepts
  • Data preprocessing techniques
  • Supervised & Unsupervised learning algorithms
  • Model evaluation techniques
  • Real-world datasets and projects
  • Advanced topics like NLP, Deep Learning, and Generative AI

Topics Covered

1. Data Analysis & Preprocessing

  • Data Cleaning
  • Handling Missing Values
  • Feature Scaling (Normalization, Standardization)
  • Encoding Techniques (Label, One-Hot)
  • Exploratory Data Analysis (EDA)
  • Visualization (Matplotlib, Seaborn)

📈 2. Supervised Learning

Regression

  • Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Ridge & Lasso Regression

Classification

  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naïve Bayes
  • Decision Trees
  • Random Forest
  • Gradient Boosting / XGBoost

3. Unsupervised Learning

  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN

4. Model Evaluation

  • Accuracy, Precision, Recall, F1 Score
  • Confusion Matrix
  • ROC-AUC Curve
  • Cross Validation
  • Bias vs Variance

5. Feature Engineering

  • Feature Selection
  • Feature Importance
  • Dimensionality Reduction (PCA)

6. Advanced Topics

  • Natural Language Processing (NLP)
  • Deep Learning Basics
  • Neural Networks
  • Model Optimization
  • Hyperparameter Tuning

7. Generative AI & Modern ML

  • LLM Basics
  • RAG (Retrieval-Augmented Generation)
  • Agentic AI Concepts

Tech Stack

  • Language: Python
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn
  • ML: Scikit-learn
  • Deep Learning: TensorFlow / PyTorch
  • Tools: Jupyter Notebook, VS Code

Project Structure

machine-learning/

├── data/
├── notebooks/
├── projects/
├── models/
├── utils/
└── README.md

How to Run

  1. Clone the repository

git clone https://github.com/your-username/machine-learning.git
cd machine-learning

  1. Install dependencies

pip install -r requirements.txt

  1. Run Jupyter Notebook

jupyter notebook

Sample Projects

  • House Price Prediction
  • Customer Churn Prediction
  • Spam Email Detection
  • Iris Classification
  • Movie Recommendation System

Learning Goals

  • Build strong ML fundamentals
  • Understand real-world problem solving
  • Implement end-to-end ML pipelines
  • Prepare for ML/AI interviews

Future Improvements

  • Add more real-world datasets
  • Deploy ML models (FastAPI + React)
  • Add Deep Learning projects
  • Build GenAI applications
  • Model monitoring & MLOps