🎓 Microsoft Elevate – GTU Internship (Jan–Apr 2026)

This repository documents my 12-week internship under the Microsoft Elevate Program, conducted as part of GTU 8th Semester Internship and powered by Edunet Foundation & FICE Education.

The internship focuses on Data Analytics, Machine Learning, Deep Learning, Power BI, Generative AI, and Microsoft Azure Cloud, with hands-on labs, notebooks, scripts, and real-world datasets.

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📌 Internship Details

• Program: Microsoft Elevate – GTU Internship
• Duration: January 2026 – April 2026 (12 Weeks)
• Mode: Online + Hands-on Labs
• Academic Requirement: GTU 8th Semester Internship
• Powered By: Edunet Foundation & FICE Education
• Mentorship: Industry experts & Microsoft trainers

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🧠 Learning Roadmap (Week-wise)

🔹 Week 1 – Foundations

• Orientation & Registration
• Introduction to Artificial Intelligence
• Introduction to Data Analytics

🔹 Week 2 – Data Handling & Visualization

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• Data Visualization (Matplotlib, Seaborn)

🔹 Week 3 – Business Intelligence

• Power BI Fundamentals
• Data Modeling
• DAX (Data Analysis Expressions)

🔹 Week 4 – Deep Learning Basics

• Artificial Neural Networks (ANN)
• Deep Learning Concepts

🔹 Week 5 – Advanced BI

• Advanced DAX
• Power BI Dashboard Development

🔹 Week 6 – Machine Learning

• Supervised Learning Algorithms
• Model Training & Evaluation

🔹 Week 7 – Advanced ML

• Unsupervised Learning
• Reinforcement Learning

🔹 Week 8 – Deep Learning (Advanced)

• ANN (Advanced Use Cases)
• Model Optimization

🔹 Week 9 – Computer Vision

• Convolutional Neural Networks (CNN)
• TensorFlow & Keras

🔹 Week 10 – Generative AI

• Generative AI Concepts
• Microsoft Copilot
• Prompt Engineering

🔹 Week 11 – Cloud Computing

• Cloud Fundamentals
• Microsoft Azure Introduction

🔹 Week 12 – Azure Experiments

• Azure Labs
• Cloud Experiments
• Deployment Basics

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📂 Repository Structure

MS-Elevate-Internship/
│
├── Data/                # Datasets used for analysis & ML
├── Notebooks/           # Google Colab / Jupyter notebooks
├── Scripts/             # Cleaned Python scripts (.py)
├── Session-Notes/       # Session-wise notes & learnings
├── README.md            # Project documentation
└── .gitignore           # Ignored files & folders

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🛠️ Technologies & Tools Used

Programming & Analysis

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

Machine Learning & AI

• Scikit-learn
• TensorFlow
• Keras
• Deep Learning (ANN, CNN)
• Reinforcement Learning
• Generative AI

Business Intelligence

• Power BI
• DAX
• Dashboard Design
• Data Modeling

Cloud

• Microsoft Azure
• Azure Labs & Experiments

Dev Tools

• Google Colab
• VS Code
• Git & GitHub

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🎯 What This Repository Shows

• My learning journey during the internship
• Hands-on data analysis & ML workflows
• Clean conversion of notebooks → scripts
• Real-world dataset handling
• Structured project organization
• Industry-ready coding practices

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🚀 Outcome

By the end of this internship, I gained:

• Strong foundation in Data Analytics & AI
• Practical exposure to Machine Learning & Deep Learning
• Experience with Power BI & Business Intelligence
• Hands-on knowledge of Azure Cloud
• Confidence in end-to-end data projects

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📌 Author

Jay Gupta
GTU 8th Semester Student
Microsoft Elevate Intern (2026)

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⭐ This repository represents my consistent effort, learning discipline, and growth throughout the Microsoft Elevate Internship.