Machine Learning and Artificial Intelligence

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Topic modeling on Chip-Seq dataset

The project is aimed at applying Latent Dirichlet Allocation (LDA) with collapsed gibbs sampling to the Chip-Seq dataset across transcription factors(TF) to identify the interactions between TFs. Left-to-right and importance sampling evaluation techniques will be used to evaluate the model perplexity.

To evaluate the correctness of the inference techniques, we implement the exact inference for a toy dataset created from a multinomial distrbution. We then compare our results from exact inference to the left-to-right and importance sampling techniques.

Technologies Used: Python, Pandas, Numpy, Scipy

Sequential minimal optimization algorithm for SVM

Implementation of SMO (Sequential minimal optimization) in Python.

Technologies Used: Python, OpenCV, Numpy

Predicting House Prices

Predicted the price of the houses based on set of features using Linear Regression consisting of historic data from May 2014 to May 2015..

Technologies Used: Python, Pandas, Numpy

Machine Learning Assignments - Short Projects

Optical Character Recognition

Created a program for Optical Character Recognition to predict numbers 3 and 5 (binary classification) using Perceptrons and Decision Trees.

Technologies Used: Python, Pandas, Numpy

Sentiment Analysis

Created a SVM model which can perform Sentiment analysis on the tweets and predict the correct sentiment of the text.

Technologies Used: Python, Pandas, Scikit-learn, NLTK

MOOCS

Machine Learning A-Z

This course introduced me to the basics concepts in Machine Learning including regression, classification, and clustering. It also touched based on some of the concepts from Reinforcement Learning and Natural Language Processing.