Data Analytics Machine Learning Artificial Intelligence
learn more Pursuing Doctor of Philosophy in Artificial Intelligence at ScAI, IIT Delhi, under the supervision of Prof.Sumeet Agarwal.
My research interests are Data science, machine learning, deep learning, and artificial intelligence. Computational Social Science is the domain I am currently working in.
Experience with Python, Data Visualization, Tableau, PyTorch, NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, Tensorflow, Deep Learning, NLP, Neural Networks, Image Classification, Plotly, Git, and more.
Look here for a collection of my latests projects, past experiences, research papers, and more.
Let me simplify my resume.
I have experience with artificial intelligence, machine learning, deep learning, data analysis and visualization, business analysis, and android application development. Always excited to learn about new products and technologies.
Comfortable with Python, SQL, Tableau, PyTorch, NumPY, Matplotlib, Seaborn, Scikit-Learn, and more, I can analyze and understand the data in a way that makes sense to the panel.
I have completed the HSL800 course at IIT Delhi that aims to equip graduate students with necessary skills needed for research writing. I'm proficient at summarizing even the most complicated concepts. I do have experience writing articles that get published. One can approach me with any existing content and material if it requires greater user-friendly readability.
Previously worked on insightful projects in the domain of machine learning, data analysis and visualization, natural language processing, AI and more.
Currently working as research scholar on project-by-project basis, will update a few of the things that I've been working on lately:
Jan 2022 - Present
The goal of this course is to train students with foundational concepts and skills in Machine Learning for high-dimensional, big data, non-Euclidean, irregular, and geometric data problems. The theory will go in conjunction with hands-on analysis of real-world applications, including ML, networks, learning, computer vision, bioinformatics, controls, etc. In the first part, mathematics for machine learning and learnability will be discussed. In the second part, machine learning techniques for high-dimensional and big data problems will be covered. The third part will introduce the rapidly evolving area of Geometric Machine Learning.
This course will present the fundamental concepts and methodologies underlying intelligent computer system design. Specific emphasis will be placed on the paradigm of statistical and decision-theoretic modelling. This course contains a large amount of programming and mathematics because these subjects are fundamental to contemporary AI.
Taught courses in AI, Deep Learning, Python programming, Data Analytics and Professional skills to both undergraduate and graduate students.
University representative for Social Media.
Headed the alumni relation team, created the website for the same.
Judge for Hackathon 2021.
Chair panels at professional conferences and present academic papers.
Aug 2019 - Jan 2021
Obtained hands-on exposure to SAP security components such as ERP Central Component 6.0 (ECC), Business Intelligence (BI), Supply Chain Management (SCM), CRM (Customer Relationship Management).
Worked as a part of large-scale projects which include security maintenance, administration and operations for various SAP environments.
Headed the alumni relation team, created the website for the same.
Experienced in preparing detailed documents and reports while managing complex internal and external data analysis responsibilities.
Obtained people and project management skills.
Jan 2019 - Aug 2019
Performed Statistical analysis and data visualisation for Lok Sabha Election - 2019 Designing data acquisition trials Assessing results and analysing trends and using statistics to make forecasts and to provide projected figures
Application of sampling techniques or utilise complete enumeration bases in order to determine and define groups to be surveyed.
Application of statistical methodology to complex data and acting in a consultancy capacity Designing and implementing data gathering/management computer systems and software
Aug 2018 - Jan 2019
Contributed analytical inputs to the Monetary Policy Committee by investigating weekly trend on Agmarket data, monthly data on IIP Index, Service indicators, PMI index, Unemployment data, Consumer Pyramid Data for the bi-monthly Monetary Policy Statement. Research, document, rate, or select alternatives for Financial Data Analysis algorithms or technologies.
Monitor developments in the fields of industrial technology, business, finance, and economic theory. Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs.
Evaluate financial reporting systems, accounting or collection procedures, or investment activities and make recommendations for changes to procedures, operating systems, budgets, or other financial control functions
Detect faces in images and videos using OpenCV and Dlib libraries
Learn how to train the LBPH algorithm to recognize faces
Track objects in videos using KCF and CSRT algorithms
Learn the whole theory behind artificial neural networks and implement them to classify images
Implement convolutional neural networks to classify images
Use transfer learning and fine tuning to improve the results of convolutional neural networks
Detect emotions in images and videos using neural networks
Detect objects using YOLO, one of the most powerful techniques for this task
Recognize gestures and actions in videos using OpenCV
Create images that don't exist in the real world with GANs (Generative Adversarial Networks)
Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering
Part 5 - Association Rule Learning: Apriori, Eclat
Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Creating a web crawler in Scrapy
Crawling a single or multiple pages and scrape data
Deploying & Scheduling Spiders to ScrapingHub
Logging into Websites with Scrapy
Running Scrapy as a Standalone Script
Integrating Splash with Scrapy to scrape JavaScript rendered websites
Using Scrapy with Selenium in Special Cases, e.g. to Scrape JavaScript Driven Web Pages
Building Scrapy Advanced Spider
Storing data extracted by Scrapy into MySQL and MongoDB databases
Several real-life web scraping projects, including Craigslist, LinkedIn and many others
The topics that intrigued me most included :
Creating a Robust Geodemographic Segmentation Model
Performing Data Mining in Tableau
Using Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
Applying the Cumulative Accuracy Profile (CAP) to assess models
Understanding the intuition of multicollinearity
Applying three levels of model maintenance to prevent model deterioration
Artificial Neural Networks to solve a Customer Churn problem
Convolutional Neural Networks for Image Recognition
Recurrent Neural Networks to predict Stock Prices
Self-Organizing Maps to investigate Fraud
Boltzmann Machines to create a Recomender System
Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize
NumPy for Numerical Data, Matplotlib for Python Plotting and Pandas for Data Analysis
Natural Language Processing and Spam Filters
Plotly for interactive dynamic visualizations
Support Vector Machines
Seaborn for statistical plots
The program covered most advanced features of data preparation in Tableau 10, where we did create table calculations, treemap charts, and storylines. We learned to tackle how to use aggregations to summarize information and also use granularity to ensure accurate calculations.
This course fully prepared me to collect, examine, and present data for any purpose, whether working with scientific data or I want to make forecasts about buying trends to increase profits.
I completed this program during the training period of my work at Deloitte USI, working as SAP Secruity Consultant with 3M.
It empowered me in the concepts of SAP Security and Authorizations.
Statistics - Machine Learning : Central Tendency, Measures Dispersion, Confusion Matrix, Accuracy and Kappa
Introduction to Payment Fraud and Machine Learning in Payment Fraud
Machine Learning in Malware and Phishing
Machine Learning in Spam
Machine Learning in Twitter Bot Detector
Machine Learning in Medical Fraud Detection
Aug 20121 - Present
Doctor of Philosophy - Artificial Intelligence
Research interest : Cognitive psychology and computational linguistics
Courses completed: Mathematical Foundations of Computer Technology, Data Mining, Advanced Machine Learning, Special Topics in Computer Applications - Social Computing, Research Writing
C.G.P.A. - 8.5
July 2017 - June 2019
Master of Technology - Data Analytics
Courses: Parallel and distributed databases, Machine learning and data mining, Cloud based big data systems, Data science programming, Design innovation and incubation, Large scale graph algorithms and analytics, Deep Learning,Emperical research and performance, Information integration and visualisation
C.G.P.A. - 7.6
May 2013 - June 2017
Bachelor of Engineering - Computer Science
Yardi School of Artificial Intelligence (ScAI)
Room 402, School of IT Building
Indian Institute of Technology Delhi
Hauz Khas, New Delhi, 110016, India
Phone: on request