This repository contains my structured journey of learning and implementing core machine learning algorithms with a strong focus on practical understanding, experimentation, and evaluation.
The goal of this repository is not just to "cover algorithms", but to understand when to use them, when they fail, and how to apply them to real-world datasets in a clean and structured way.
- Linear Regression
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Tree
- Random Forest
- Gradient Boosting
- K-Means Clustering
- Principal Component Analysis (PCA)
- Naive Bayes
- Support Vector Machine (SVM - Basic Understanding)
Each algorithm folder contains:
- Short conceptual notes (problem it solves, strengths, limitations)
- Clean implementation notebook
- Dataset used for experimentation
- Evaluation metrics and comparison
- Observations and mistakes encountered
The focus is on:
- Data preprocessing
- Model training
- Performance evaluation
- Practical intuition over theoretical memorization
Each algorithm has its own dedicated folder containing:
- Notes (
.md) - Implementation notebook (
.ipynb) - Dataset used for practice
- Supporting files (if required)
There is also a 00-ml-concepts folder covering foundational ideas like:
- Bias vs Variance
- Overfitting & Underfitting
- Train-Test Split
- Evaluation Metrics
This repository is being built as part of a focused preparation plan toward:
- Machine Learning / Data Science Internship roles
- Strong practical ML foundation
- Building industry-ready project thinking
The emphasis is on clarity, structure, and real understanding rather than superficial coverage.
- Python
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
This is an actively evolving repository. Improvements, refinements, and additional experiments will be added as learning progresses.