A structured collection of my Data Science learning journey — covering hands-on practice, concept notebooks, and end-to-end projects across Python, SQL, ML, DL, NLP, and LLMs.
| Folder | Description |
|---|---|
PythonLearning |
Core Python concepts, data structures, and practice notebooks using NumPy, Pandas, Matplotlib & Seaborn |
SQL Learnings |
SQL queries from basics to advanced — joins, subqueries, aggregations, and analytical use cases |
Machine Learning |
Supervised & unsupervised ML models (Linear/Logistic Regression, KNN, SVM, Decision Trees, etc.) with preprocessing and evaluation |
Deep Learning |
Neural network implementations — ANN, CNN, and RNN using TensorFlow/Keras |
NLP |
Natural Language Processing concepts and practice notebooks |
Major Projects |
End-to-end projects including Placement Prediction, Python Finder, and a Blogging Website (SQL) |
DataSets |
Raw and cleaned datasets used across notebooks and projects |
Resources |
Reference materials, learning notes, and useful documentation |
Python · SQL · Jupyter Notebook · scikit-learn · Pandas · NumPy · Matplotlib/Seaborn · TensorFlow/Keras
Personal learning repository to document progress, practice concepts, and build a strong foundation in data science and machine learning.
Feel free to explore, fork, or reach out via GitHub for suggestions or collaboration.