Advanced Model Architectures & Language AI

About this course

Take your data analysis skills to the next level by building, evaluating, and deploying the advanced models that power real-world AI systems. In this course, you'll work with decision trees, ensemble methods, neural networks, large language models, and conversational AI — integrating techniques that data professionals use to solve complex, production-grade problems. You'll move from training and pruning tree-based models to quantifying ensemble lift, from diagnosing overfitting in neural networks to fine-tuning LLMs on domain-specific data. You'll also build a retrieval-augmented chatbot and evaluate NLP pipelines end to end. By the end, you'll be able to recommend deployment-ready solutions, communicate model decisions to stakeholders, and demonstrate the breadth of skills that employers look for in intermediate-to-advanced data analyst and machine learning roles.

What you will learn

• Build and evaluate ensemble methods including bagging, boosting, and stacking using Python and scikit-learn.
• Develop and regularize feed-forward neural networks using Keras and PyTorch to meet validation loss targets.
• Create automated data-to-text pipelines using SQL, Python, and LLM APIs to generate business narrative summaries.
• Build RAG-powered chatbots and apply NLP techniques including NER and text vectorization using spaCy and HuggingFace.