Harshika Hooda

Innovative Artificial Intelligence Engineer possessing strong mathematical skills and detailed knowledge of machine learning evaluation metrics and best practices. Offering 1 year of experience creating programs and algorithms to enable machines to take actions without being directed. Expertise in predictive analysis, data mining and computational statistics. Logical and detailed professional with exceptional Python coding and typed language skills such as Java and C++.

• R programming Fundamentals, Stanford University
• Data Analysis in Python, IBM
• Google Cloud Big Data and Machine
  Learning Fundamentals, Google
• Getting started with Data Analytics on AWS, AWS

• Investigatory Project on AQI - A comparison of AQI levels in major Indian cities post and pre COVID-19
• Bacteria Segmentation using SegNet - The main aim of this project is to monitor the amount of bacteria present and classify the different types of water bodies. I used SegNet to identify the amount of bacteria, as it uses 13 layers to work.
• Pesticide Sales Prediction in Europe using Big Data techniques - This project was designed to study forecasting based on BigData methods through the example of pesticide sales. I used predictions based on linear regression and two frameworks for building neural networks, as well as obtain statistics and compare results.
• Digital Steganographer - The project provides a graphical user interface (GUI), that makes it simple to send sensitive information without being detected by hackers. This system is adaptable and platform- independent, and it supports all Java features.

• Investigatory Project on AQI - A comparison of AQI levels in major Indian cities post and pre COVID-19
• Bacteria Segmentation using SegNet - The main aim of this project is to monitor the amount of bacteria present and classify the different types of water bodies. I used SegNet to identify the amount of bacteria, as it uses 13 layers to work.
• Pesticide Sales Prediction in Europe using Big Data techniques - This project was designed to study forecasting based on BigData methods through the example of pesticide sales. I used predictions based on linear regression and two frameworks for building neural networks, as well as obtain statistics and compare results.
• Digital Steganographer - The project provides a graphical user interface (GUI), that makes it simple to send sensitive information without being detected by hackers. This system is adaptable and platform- independent, and it supports all Java features.