Intro to Machine Learning

Award of Final Exam Completion - Database Foundations

Certificate of Participation - Xiaomi Ode2Code

Pandas

Data Analysis with Python

Java (Basic) Certificate

Data Cleaning

Software Engineering Intern

Software engineering lite job simulation

Python (Basic) Certificate

Member

Bachelor of Technology - BTech

* Completed a simulation focused on the process of completing an engineering ticket for a system in the credit-card rewards department of JPMorgan Chase & Co. * Created a new class to get an existing system up and running * Wrote a test suite for the class added

Virtual simulation

Member | Contributor

Contributor

Contributed to the development of a federated learning model for decentralized medical image classification and segmentation. Addressed challenges associated with distributed data privacy, heterogeneity, and communication efficiency. Proposed and implemented improvements to enhance performance and alignment with project objectives. Understood and worked with complex machine learning workflows, including model aggregation strategies and secure computation techniques.

Student Project Intern

Includes the following sections and activities: Importing Libraries: The notebook begins by importing essential libraries such as pandas, numpy, matplotlib, scikit-learn modules (train_test_split, mean_squared_error, r2_score), and the Ridge regression model. Loading the Dataset: It loads a dataset from a CSV file located at /kaggle/input/training-for-mri/final_merged_file.csv. Gaining Insights from the Dataset: This section is dedicated to data exploration, including basic statistics and visualizations to understand the dataset's structure and features. The notebook contains the following sections and steps: Importing Libraries: Various essential libraries are imported, including pandas, numpy, matplotlib, and scikit-learn. Loading the Dataset: The dataset is read from a CSV file located at /kaggle/input/training-for-mri/final_merged_file.csv. Exploratory Data Analysis (EDA): Inspecting data types. Checking for missing values. Visualizing distributions of the target variable and featu

ML Engineer | Ex-ML Research & Development @IIT-H (Vigil Labs) | Data Scientist | MLOps | Production Systems

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Know about Divyansh Pandey's work experience, education, and much more. Divyansh Pandey