Make Machine Learning Models Transparent, Reliable, and Scientifically Meaningful.
Interpretable ML for Scientific Discovery is a 3-day online hands-on workshop designed to help participants build, evaluate, and interpret machine learning models for scientific data analysis.
The workshop focuses on using Scikit-learn, SHAP, and Yellowbrick to understand model performance, identify influential variables, visualize diagnostics, and extract meaningful scientific insights from machine learning predictions.
The aim of this workshop is to help participants develop practical skills in interpretable machine learning for scientific discovery. Participants will learn how to build ML models, evaluate their reliability, explain model behavior, and connect model outputs with scientific reasoning.
Role of machine learning in scientific discovery
Understanding scientific datasets, variables, features, and labels
Framing scientific problems as ML tasks
Data preprocessing and train-test split workflow
Building baseline models using Scikit-learn
Model evaluation and reliability interpretation
Understanding how ML results support scientific decision-making
🛠️ Hands-on:
Hands-on 1: Load and prepare a scientific-style dataset using Scikit-learn
Hands-on 2: Build and evaluate a baseline ML model using Scikit-learn
🧰 Tools Covered: Scikit-learn, Python, Jupyter Notebook / Google Colab
Introduction to interpretable machine learning
Understanding feature importance in scientific models
Built-in feature importance vs permutation importance
Using permutation importance to identify influential variables
Yellowbrick visualizations for model diagnostics
Interpreting influential variables in scientific datasets
Using visualization to support model reliability and interpretation
🛠️ Hands-on:
Hands-on 1: Perform feature importance analysis using Scikit-learn
Hands-on 2: Apply permutation importance and create Yellowbrick visualizations
🧰 Tools Covered: Scikit-learn, Yellowbrick, Python, Jupyter Notebook / Google Colab
Introduction to SHAP for explainable AI
Local vs global model interpretation
SHAP summary plots for global explanations
SHAP explanations for individual predictions
Connecting model explanations with scientific reasoning
Guided final exercise for scientific insight extraction
Preparing a short scientific interpretation from model explanations
🛠️ Hands-on:
Hands-on 1: Generate SHAP explanations for global model interpretation
Hands-on 2: Interpret individual predictions and prepare a short scientific insight summary
🧰 Tools Covered: SHAP, Scikit-learn, Yellowbrick, Python, Jupyter Notebook / Google Colab
Basic knowledge of Python or machine learning is helpful, but advanced expertise is not mandatory.
June 2, 2026
IST 4.30 pm
June 2, 2026 – June 4, 2026
IST 5.30 PM
₹2,499
$50
The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
Live, mentor-led and certified. Dates and times in IST.
