Module 1 of 5·2 hours

Introduction to Machine Learning

Understand what machine learning is, its types, and where it fits in the AI landscape.

Learning Objectives
  • Define machine learning and distinguish it from traditional programming
  • Identify the three main types of machine learning
  • Recognize real-world applications of ML across industries
  • Set up a basic ML development environment
20 min

What is Machine Learning?

What is Machine Learning?



Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. Instead of writing rules, we feed data to algorithms that discover patterns on their own.

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Traditional Programming vs. Machine Learning



Traditional ProgrammingMachine Learning |---|---| Input: Data + RulesInput: Data + Expected Output Output: ResultsOutput: Rules (Model) Programmer defines logicAlgorithm discovers logic

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A Simple Example



Consider email spam filtering:
  • Traditional approach: Write rules like "if email contains 'buy now' → spam"
  • ML approach: Show the algorithm 10,000 labeled emails, let it learn what makes spam


    • The ML approach adapts to new spam patterns automatically, while traditional rules need constant updating.

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    Key Terminology

  • Model: The learned representation of patterns in data
  • Training: The process of feeding data to an algorithm
  • Features: Input variables used for predictions
  • Labels: The target variable we want to predict
  • Inference: Using a trained model to make predictions on new data

    • Knowledge Check

    1. What is the primary difference between traditional programming and machine learning?

    2. What is 'training' in the context of machine learning?

    Lesson 1 of 4