Foundations of Applied Machine Learning

This unit establishes practical foundations in machine learning by introducing core concepts, common algorithms (perceptron, decision trees), data roles, and ethical/data-permission considerations. Learners set up essential tools (Google Account, Google Colab), examine hardware options, and practice data preparation and basic model-building techniques to prepare for deeper algorithmic and applied units.

Learning Objectives

• Analyze ethical, legal, and source-related considerations for collecting and using datasets by identifying permission requirements and appropriate data sources
• Apply fundamental ML tools and workflows by creating a Google account, configuring Google Colab, and selecting suitable hardware options for basic experiments
• Demonstrate understanding of core algorithms and concepts by implementing and explaining perceptron and decision tree models, similarity measures, and simple feature combinations on example datasets
• Evaluate data preparation and evaluation techniques by performing feature engineering, data augmentation, and interpreting learning curves to design suitable datasets for NLP and e-commerce prototypes