Machine Learning
Quora question Pair Similarity Problem - NLP Based
Using Machine learning to predict question asked by user is duplicate or not by saving time for other user to answer the question using logistic regression,SVM,Xgboost and deployed using Flask.
Alogorithm Used: Logistic regression,SVM,Xgboost.
Technologies Used: Python,Pandas,Scikit-Learn,Flask.
Personalized Cancer Diagnosis
Classify the given genetic variations/mutations based on evidence from text-based clinical literature. In this problem, we need to find the mutation-type given the gene, variation and some text data from published research.
Technologies Used: Python,Pandas,Scikit-Learn,Flask.
Facebook Friend Recommendation using Graph Mining
Recommending friend a facebook user from its mutual friends using Machine learning
Technologies Used: Python,Pandas,Scikit-Learn,Flask.
Stackoverflow tag predictor- NLP Based
Suggest the tags based on the content that was there in the question posted on Stackoverflow.
Technologies Used: Python,Pandas,Scikit-Learn,Flask.
Taxi demand prediction in New York City
For a given location in New York City, our goal is to predict the number of pickups in that given location at particular time interval. Some location require more taxis at a particular time than other locations owing to the presence schools, hospitals, offices etc. The prediction result can be transferred to the taxi drivers via Smartphone app, and they can subsequently move to the locations where predicted pickups are high.
Technologies Used: Python,Pandas,Scikit-Learn,Flask.
MOOCS
Machine Learning From Applied Ai Course
This course provides a broad introduction to machine learning,deep learning, and statistical pattern recognition.
Topics include:
(i) Supervised learning (parametric/non-parametric algorithms,KNN,Naive Bayes,Linear Regression,Logistic Regression, support vector machines, kernels,Decsions tree,Ensembe Modeling, Neural networks,CNN,RNN,LSTM).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
