This repository is my personal workspace for learning and experimenting with Machine Learning concepts through hands-on coding and real datasets. Everything here is focused on building a strong foundation in ML, step by step, by actually implementing what I learn.
The idea behind this repo is simple:
Learn the theory → write the code → break things → fix them → understand ML properly.
This repo contains:
- Implementations of core Machine Learning algorithms
- Practice notebooks for important ML concepts
- Mini-projects and full projects built on real datasets
- End-to-end ML workflows: from raw data to final model
Topics covered include:
- Regression & Classification
- Supervised & Unsupervised Learning
- Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Model Evaluation & Tuning
- Overfitting, Underfitting & Bias–Variance tradeoff
I created this repository to:
- Truly understand ML fundamentals, not just use libraries
- Maintain a clean record of my learning progress
- Build a strong project-based ML portfolio
- Prepare for internships, research work, and future roles in ML/AI
This is a learning-first repository, so you may see multiple approaches to the same problem as my understanding improves over time.
Most of the work here is done using:
- Python
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
More advanced tools and deployment frameworks will be added as I grow.