Advanced Machine Learning & AI: Concepts and Real-World Applications

To empower participants with foundational and advanced concepts in Machine Learning and Artificial Intelligence, fostering real-world application skills and preparing them for dynamic roles in intelligent systems and data-driven industries.

Day 1 – Foundations of Modern Machine Learning ● Evolution of Machine Learning: Classical to Intelligent Systems ● Supervised, Unsupervised & Self-Supervised Learning Techniques ● Structuring ML Pipelines: Data Preprocessing to Model Deployment ● Hands-On: Training your first ML model using scikit-learn on real-world data Day 2 – Deep Learning & Transfer Learning in Action ● Understanding CNNs, RNNs, Transformers – Architectures that Matter ● Leveraging Transfer Learning with Pretrained Models (ResNet, BERT) ● Best Practices for Fine-Tuning and Customization ● Hands-On: Fine-tuning a transformer model for text classification using Hugging Face Day 3 – ML Ethics, Interpretability & Applications ● Responsible AI: Fairness, Bias Mitigation, and Compliance ● Interpreting ML Decisions: SHAP, LIME, and XAI Tools ● Industry Use Cases: Healthcare, Finance, Smart Cities ● Hands-On: Interpreting model predictions using SHAP on a sensitive dataset