Introduction to Machine Learning
Machine learning is the practice of teaching computers to make predictions or decisions from data, rather than programming explicit rules. This module introduces the conceptual framework of ML — what it is, what it is not, and how to approach a machine learning problem methodically.
The module establishes the four-phase ML workflow — data collection, cleaning, feature engineering, and modeling — that all subsequent ML modules follow. By the end, you will have trained your first model and understood what goes into making it reliable.
Lessons
- Introduction to Machine Learning — What ML is and how it differs from traditional programming; the supervised learning setup; an overview of the ML workflow.
- Supervised vs. Unsupervised Learning — The distinction between labeled and unlabeled learning, with examples of each; regression and classification as the two main supervised tasks.
- Collecting Data — Where data comes from, evaluating dataset quality, and the relationship between data quantity and model performance.
- Cleaning Data — Identifying and handling missing values, duplicates, and data type inconsistencies in a real dataset.
- Introduction to Feature Engineering — Encoding categorical variables, scaling numeric features, and transforming raw inputs into representations that models can learn from.
- Module Assessment — A machine learning workflow assessment using the Titanic dataset, covering cleaning, feature encoding, and training a first classifier.