Machine Learning Algorithms
Analysis of Machine Learning Algorithms
Proficiency in machine learning algorithms necessitates a profound understanding of foundational mathematical disciplines, specifically linear algebra, calculus, and probability theory.
Within this repository, a comprehensive exploration of machine learning algorithms will be undertaken, focusing on three principal facets aligned with the aforementioned mathematical domains.
Algorithms covered:
- Linear & Polynomial Regression
- Logistic Regression
- Support Vector Machine (SVM)
- Locality Sensitive Hashing - Random projection
- K Nearest Neighbors (Classifiers & Regressor)
- Dimensionality Reduction (PCA, LDA, Auto-Encoder, CCA)
- Clustering (K-Means Clustering)
- Gaussian Mixture Models (GMM), Expectation Maximization (EM) Algorithms
- Spectral Clustering
- Generative Modeling (GAN)
- Diffusion Model
- Deep Learning
- Reinforcement Learning