📚 Syllabus & Course Curriculum
Nanotechnology & Materials Science
Module-by-module breakdown of Data-Driven Materials Discovery Using Machine Learning, from foundations to a certified capstone project.
Outline
Understand materials data and load a real‑world dataset in Google Colab • Clean and preprocess data using Pandas • Convert composition into machine‑learnable features
Outline
Build regression models (Linear Regression, Random Forest) for property prediction • Engineer features tailored to materials datasets • Evaluate models using R², MAE and visual diagnostics
Outline
Improve model performance with tuning and validation techniques • Analyze feature importance and interpret results • Generate publication‑ready plots and export a complete case study
Earn government-registered certification in Data-Driven Materials Discovery Using Machine Learning
e-Certificate and e-Marksheet issued on successful completion.
