Why this course?

Description

deep dive into supervised and unsupervised machine learning using Python. It equips learners with practical exposure to real-world classification and regression tasks, preprocessing pipelines, and model deployment workflows.

Learning Outcomes

• Develop a foundational understanding of machine learning algorithms
• Practice data preprocessing, visualization, and feature engineering
• Implement and evaluate supervised ML models
• Build end-to-end ML applications

Daily Breakdown

• Day 1: Introduction to ML (Presentation + Video)
• Day 2–3: Python Programming for ML – I & II (Practice Code Included)
• Day 4–5: Pandas & Numpy for ML
• Day 6: Data Visualization
• Day 7–8: Data Collection and Preprocessing (Part I & II)
• Day 9: Train-Test Model Development
• Day 10–28: Mini Projects (e.g., Iris Classification, Rainfall, Titanic, Fake News, Credit Card Fraud, Heart Disease, etc.)
• Day 29: Covid-19 Prediction Using CNN
• Day 30: Model Deployment

Notable Projects

• Iris Flower Classification
• Loan Status and Rainfall Prediction
• Fake News Detection
• Credit Card Fraud and Heart Disease Prediction
• Sentiment Analysis & Electricity Price Prediction
• Customer Segmentation & Movie Recommendation
• Covid-19 Detection Using CNN
• Final Project: End-to-End Model Deployment

Internship Report Guidelines

Report Structure

Title Page

• Student details, mentor, project title

Abstract

• One-paragraph summary

Introduction

• Scope and purpose of the internship

Modules Covered

• Summary of tools and techniques

Case Studies and Projects

• Problem, implementation, outcomes

Insights and Learnings

• Tools mastered, challenges overcome

Conclusion

• Future applications of skills

References

• Cited datasets, libraries, documentation

Appendices

• Screenshots, code samples, charts