This repository contains my personal notebooks and mini-projects from the Python for Data Science and Machine Learning Bootcamp by Jose Portilla. The repository is organized topic-wise, with each topic folder containing:
- The corresponding notebook
- Mini-projects (if any)
- Dataset(s) used (if applicable)
These notebooks serve as a personal revision archive and reference for future research work, projects, and internship preparation.
- ✅ Python Basics
- ✅ NumPy & Pandas (Data Manipulation)
- ✅ Matplotlib & Seaborn (Data Visualization)
- ✅ Introduction to Machine Learning
- ✅ Bias-Variance Tradeoff
- ✅ Logistic Regression & K-Nearest Neighbors (KNN)
- ✅ Decision Trees & Random Forests
- ✅ Support Vector Machines (SVM) & K-Means Clustering
- ✅ Principal Component Analysis (PCA)
- ✅ Recommender Systems
- ✅ Natural Language Processing (NLP)
- ✅ Neural Networks & Deep Learning
- ✅ Mini-Projects & Practice Exercises