1 - Applied Statistics

This project uses Plotting distribution, Visualization and Hypothesis Testing to validate statistical evidence and leverage information to make effective decisions

• Part 1- Answering Industry Problems through Statistical inferences
• Part 2- Analyze past tournament information to make informative investment decisions.
• Part 3- Analyzing the status of various startups that participated in the Startup Battlefield which is the world’s pre-eminent startup competition. Domain- Sports; Startup industry

Skills and Tools

• EDA,
• Data Visualization,
• Statistical Inference,
• Hypothesis Testing,
• Python

2 - Supervised Learning

This project uses the most popular classification techniques to predict the outcomes after an extensive EDA and work missing values, and imbalance in data. This project has two parts:

• Part 1 - Predicting the condition of the patient depending on the received test results on biomechanics features of the patients according to their current conditions.
• Part 2 - Build an AIML model to perform focused marketing by predicting the potential customers who will convert using the historical database.

Skills and Tools

• Logistic Regression,
• Naive Bayes,
• KNN,
• Classification

3 - Ensemble Techniques

The project was accomplished by deploying a GUI powered by supervised learning and ensemble modelling techniques to build and train a prediction model for a telecom company. This will help them identify the potential customers who have a higher probability to churn. This will enable the company to understand and pinpoints the patterns of customer churn and increase the focus on customer retention strategies.

Skills and Tools

• EDA
• Logistic regression
• Decision Trees
• Random forest
• boosting

4 - Unsupervised Learning

Cluster the vehicles based on fuel consumption attributes to train a regression model Generating synthetic data using existing partially labelled data provided for a wine manufacturer Curate a Dimensionally Reduced data to build a supervised learning classification model for an automobile dataset Based on historical data Design a performance ranking model for professional cricket players in IPL Work on various multimedia data for dimension reduction.

Skills and Tools

• Clustering,
• Support Vector Machines,
• Principal Component Analysis,
• Classification

5 - Featurization & Model Selection & Tuning

The project was accomplished by employing supervised learning, ensemble modelling and unsupervised learning techniques to build and train a prediction model to identify Pass/Fail yield of a particular process entity for a semiconductor manufacturing company. This projects helps to determine key factors contributing to yield excursions downstream in the process and will enable an increase in process throughput, decreased time to learning and reduce the per unit production costs.

Skills and Tools

• Supervised learnin
• PCA
• Feature selection
• Model tuning
• Grid Search

6 - Recommendation Systems

This project replicates a real time use case of an e-commerce company, which can recommend mobile phones to a user, which are most popular and personalized respectively. The project was accomplished by employing recommendation techniques such as popularity based recommendation and collaborative filtering methods to recommend a mobile handset to its users based on the individual consumer’s behavior/choice. Domain- Electronics, E-commerce

Skills and Tools

• Collaborative Filterin
• Popularity-based
• Recommender Systems
• Python

7 - Neural Networks & Deep Learning

Course - Introduction to Neural Network and Deep Learning

The project was accomplished by delivering 4 sub-projects.

• Part 1,2 & 3 deploys a GUI powered by neural network to build a regressor & classifier respectively for a communications equipment manufacturer. The model predicts the equipment’s signal quality using various parameters from its products, which is responsible for emitting informative signals.
• Part 4 delivers an image classifier, which can classify numbers from the photographs captured at street level using a Neural Network

Skills and Tools

• Autonomous Vehicles
• Electronics and Telecommunication
• Neural Networks
• Deep Learning
• TensorFlow
• Image Recognition
• GUI

8 - CNN Architecture and Transfer Learning

Course - Computer Vision

This project involves 5 sub projects including a CV powered GUI to solve the problem of a botanical research group.

Part 1 Image classifier capable of determining a plant's species.

Part 2 Detailed analysis on how CNN is a better image classifier over traditional methods

Part 3 Curating an image dataset for a brand research company

Part 4 Image classifier capable of determining a flower’s species

Part 5 Submit your strategy to maintain and support the AIML the model in production

Skills and Tools

• Botanical Research
• Automobile
• Computer Vision
• CNN
• Transfer Learning
• TensorFlow
• GUI

9 - Computer vision - absolutely fabulous time learning it

10 - NLP - refer to sarcasm detection repo in my profile