MACHINE LEARNING & ARTIFICIAL INTELLIGENCE

Implemented and Optimized problems based on Financial Data, Biomedical Data, Signal Processing, Computer Vision and NLP data to build models for regression and classification. Implemented algorithms for Supervised and Unsupervised Learning using Scikit-Learn, TensorFlow and Pytorch as well as from scratch using Numpy, Scipy, Matplotlib and Seaborn.

Implemented various Heuristic Search, Criteria based optimizations, Markov Chains, Viterbi Algorithms and MCMC (Markov Chain Monte Carlo), Neural Networks, SVM, Decision Trees and Ensemble Learning techniques on data from various domains.

Dimensionality Reduction Techniques

Implemented Dimensionality Reduction techniques like PCA, NMF and ICA as functions to be used for ML problems.

Source Separation Techniques

Implemented Source Seperation Algorithm for the multiple as well as single source audio clips, achieving a good SNR.

Clustering

Implemented K Means, Gaussian Mixture Models and KNN methods from scratch to be used on a variety of data sets.

Classification & Regression

Solved Classification problems using KNN, Markov Chains, Viterbi Algorithm and Single Layer and Multi-layer Perceptron Designs.

Heuristic Search

Search algorithms with Heuristics, N Queens problem, Minimax Algorithm.

Image Boundary Finding & Part of Speech Tagging

Implemented Algorithms to find the Image Boundaries and Parts of Speech using concepts of Bayesian Theory, Markov Chains and Viterbi Algorithm.

Predictive Analysis

Implemented Linear Regressor, Decision Tree Regressor, SVM Regressor and Neural Network Regressor to predict the market value of a player using the FIFA Dataset