Machine_Learning_Basics
This repository contains implementations of fundamental machine learning algorithms and techniques. The codes are written primarily from scratch, with some algorithms also implemented using scikit-learn for reference and comparison. The repository is structured to help beginners understand the core concepts and workings of these algorithms in Python.
📁 Repository Structure
.ipynb_checkpoints/: Auto-generated checkpoints for Jupyter Notebook files.
DimensionalityReduction/: Contains notebooks related to dimensionality reduction techniques like PCA.
K-Mean/: Implementation of the K-Means clustering algorithm from scratch and using scikit-learn.
KNN/: Implementation of the K-Nearest Neighbors algorithm.
LinearRegression/: Implementation of linear regression for single/multiple features, including a notebook on logistic regression.
Logistic_Regression/: Implementation of logistic regression techniques.
PCA/: Principal Component Analysis algorithm implementation and example usage.
README.md: Documentation file.
🧰 Requirements
Make sure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn seaborn
🖥️ Algorithms Implemented
Linear Regression
From scratch implementation.
Single and multiple feature regression.
Evaluation metrics like MSE and R².
Logistic Regression
From scratch and using scikit-learn.
Binary classification problems.
K-Nearest Neighbors (KNN)
From scratch implementation.
Euclidean distance-based classification.
K-Means Clustering
From scratch implementation.
Using scikit-learn for reference.
Visualization of cluster formation.
Principal Component Analysis (PCA)
Dimensionality reduction for datasets with high features.
Visualization in reduced dimensions.
Other Dimensionality Reduction Techniques
Implementations and examples in DimensionalityReduction/ folder.
⚙️ Usage
Clone the repository:
git clone https://github.com/GauravSingh0248/Machine_Learning_Basics.git
cd Machine_Learning_Basics
Open the Jupyter Notebook corresponding to the algorithm you want to study/run:
jupyter notebook
Follow the step-by-step code cells and modify datasets or parameters as needed.
📚 Learning Objectives
Understand the mathematical foundation of basic machine learning algorithms.
Learn how to implement algorithms from scratch in Python.
Compare scratch implementations with scikit-learn versions for performance and accuracy.
Gain hands-on experience with data visualization and model evaluation.
📷 References
Scikit-learn documentation
reachout -- [email protected]