Course Topics
Explore all 14 machine learning topics covered in this course.
Foundations
| Topic | Description |
|---|---|
| ML Foundations | Introduction to machine learning concepts and the learning journey |
| Supervised Learning | Prediction, regression, and classification fundamentals |
| Unsupervised Learning | Discovery without labels, pattern finding |
| Neural Networks | Deep learning architectures and training |
Core Techniques
| Topic | Description |
|---|---|
| Clustering | K-means, DBSCAN, hierarchical clustering for customer segmentation |
| Classification | Decision trees, random forests for categorization |
| NLP & Sentiment | Text analysis and sentiment classification |
| Topic Modeling | LDA and document topic extraction |
Advanced Applications
| Topic | Description |
|---|---|
| Generative AI | GPT, prompting, and content generation |
| Structured Output | JSON output and reliable AI responses |
| Validation & Metrics | Model evaluation and performance measurement |
| A/B Testing | Statistical testing and experimentation |
Specialized
| Topic | Description |
|---|---|
| Responsible AI | Ethics, fairness, SHAP explanations |
| Finance Applications | VaR, portfolio optimization, risk modeling |
(c) Joerg Osterrieder 2025
ML Foundations
Machine Learning Foundations The starting point for understanding how machines learn from data. Learning Outcomes By …
Learn more →Supervised Learning
Supervised Learning Learning from labeled examples to make predictions on new data. Learning Outcomes By completing this …
Learn more →Unsupervised Learning
Unsupervised Learning Discovering patterns in data without predefined labels. Learning Outcomes By completing this …
Learn more →Neural Networks
Neural Networks Deep learning architectures for complex pattern recognition. Learning Outcomes By completing this topic, …
Learn more →Clustering
Clustering Discovering natural groupings in data without predefined labels. Learning Outcomes By completing this topic, …
Learn more →NLP & Sentiment Analysis
NLP & Sentiment Analysis Extracting meaning and emotion from text data. Learning Outcomes By completing this topic, …
Learn more →Classification
Classification Categorizing data into predefined classes using tree-based methods. Learning Outcomes By completing this …
Learn more →Generative AI
Generative AI Creating new content with large language models and AI systems. Learning Outcomes By completing this …
Learn more →Topic Modeling
Topic Modeling Discovering abstract topics in document collections. Learning Outcomes By completing this topic, you …
Learn more →Responsible AI
Responsible AI Building AI systems that are fair, transparent, and accountable. Learning Outcomes By completing this …
Learn more →Structured Output
Structured Output Generating reliable, formatted AI responses for production systems. Learning Outcomes By completing …
Learn more →Validation & Metrics
Validation & Metrics Evaluating and measuring model performance systematically. Learning Outcomes By completing this …
Learn more →A/B Testing
A/B Testing Statistical experimentation for data-driven decisions. Learning Outcomes By completing this topic, you will: …
Learn more →Finance Applications
Finance Applications ML applications in financial services and risk management. Learning Outcomes By completing this …
Learn more →Innovation Diamond
Innovation Diamond Capstone presentation integrating all course concepts into a complete ML-powered innovation pipeline. …
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