Advanced Model Architectures & Language AI

Overview

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.

Syllabus

• Decision Tree Construction & Pruning - Foundation
Build and prune CART models with stakeholder-ready visualizations
• Ensemble Methods Comparison - Core Application
Apply bagging, boosting, and stacking on the same dataset, compare metrics, and quantify ensemble lift over single models
• Computational Cost Assessment - Integration & Assessment
Evaluate computational cost vs. performance gain for each ensemble technique and recommend deployment feasibility
• Feed-forward Network Implementation - Foundation
Build a feed-forward neural network using Keras/PyTorch, achieve a specified validation loss, and document architecture choices.
• Overfitting Evaluation & Regularization - Core Application
Evaluate overfitting via learning-curve analysis and implement regularization (dropout/L2) to meet generalization targets.
• Executive Brief Generation - Foundation
Learners will apply LLMs to generate first-draft executive briefs that summarize model insights and refine prompts to meet specified ROUGE or BLEU scores.
• Data-to-Text Pipelines - Core Application
Learners will create comprehensive data-to-text pipelines that combine SQL, Python, and LLM APIs to automatically transform KPI (Key Performance Indicator ) tables into narrative summaries.
• LLM Fine-tuning & Evaluation - Integration
Learners will fine-tune small LLMs on company FAQs and measure improvement in response relevance through systematic human evaluation.
• Cost-Performance Analysis - Assessment
Learners will evaluate cost vs. latency trade-offs between open-source and commercial LLMs for real-time chat applications through systematic analysis.
• RAG Chatbot Development - Foundation
Build a chatbot prototype using RAG (retrieval-augmented generation) and measure user satisfaction through SUS survey.
• Dialog Flow Optimization - Core Application
Evaluate dialog-flow metrics (fallback rate, turn length) and iterate on intent-matching rules.
• Named Entity Recognition - Integration
Apply named-entity recognition to extract key terms from support tickets and quantify precision/recall.
• Text Vectorization Evaluation - Assessment
Evaluate two vectorization techniques (TF-IDF vs. embeddings) on a text-classification task.
• Project: Advanced Model Architectures & Language AI
You build an end-to-end AI-powered insights application that combines ensemble modeling with LLM-driven explanation generation. You train and evaluate an ensemble model to predict customer health scores, extract feature importance, and integrate a large language model to generate natural language explanations for each prediction. The final deliverable is a functioning application with a simple interface and technical documentation suitable for a development team.

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Taught by

Professionals from the Industry

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Subjects

Computer Science