Overview

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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.