A collection of machine learning algorithms implemented from scratch and using popular frameworks, with applications to real-world datasets.
- From-Scratch Implementations of core ML algorithms
- Optimized Versions using scikit-learn/PyTorch
- Jupyter Notebooks with detailed explanations
- Benchmark Comparisons (scratch vs. library)
- Production-ready code samples
- Comprehensive coverage of Supervised & Unsupervised Learning algorithms
Classification:
- K-Nearest Neighbors (KNN)
- Logistic Regression (Binary & Multi-class)
- Naive Bayes (Gaussian, Multinomial)
- Perceptron Algorithm
- Support Vector Machines (SVM)
- Decision Trees
- Random Forests
- Neural Networks (Feedforward Basics)
Regression:
- Linear Regression (Simple & Multiple)
- Polynomial Regression
- Logistic Regression (for classification tasks)
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Principal Component Analysis (PCA)
- Students and beginners learning ML fundamentals
- Comparing custom implementations vs. popular libraries
- Building intuition behind each algorithm
- Educational projects, assignments, and practical understanding