AI data driven machine learning in research using Low code
Topics Covered
- Day 1: Practical Foundations of Machine Learning
Introduction to ML workflows with real datasets
Setting up Python/ML environments (Scikit-learn, TensorFlow, PyTorch)
Data exploration and preprocessing (hands-on lab) - Day 2: Prediction & Model Building
Regression and boosting models
Building predictive models step-by-step
Hands-on exercises with domain-specific datasets - Day 3: Validation & Cross-Validation
Train-test split, k-fold cross-validation, and LOOCV
Avoiding overfitting and bias
Practical lab: validating models on research datasets - Day 4: Optimization & Hyperparameter Tuning
Hyper parameter for model optimization
Performance metrics (validation metrics)
Hands-on lab: optimizing models for reliability and robustness - Day 5: Interpretation & Research Integration
Model explainability (SHAP, PDP, feature importance)
Translating ML outputs into research insights
Designing reproducible ML workflows for publication and collaboration
Reflection, peer learning, and roadmap for ML-driven research impact
Registration Fee
Indian Participants: ₹ 499
Foreign Participants: $ 08
Note: ✅Participants will receive the meeting details 24 hours before the event
✅ Participants will receive electronic certificates and video recording links after completion of the workshop.