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Machine Learning in Practice

Train, test and evaluate simple models and recognise when they go wrong.

⏱ 10.0 hours📚 foundation level

What you'll learn

  • Prepare and split a dataset for training and testing
  • Train and evaluate a simple classifier
  • Recognise overfitting and basic ways to reduce it

Course syllabus16 lessons

Machine Learning in Practice

0/5 lessons
  1. 1📖Neural Networks
  2. 2📖❓Training Pipelines🔒
  3. 3📖🔬Feature Engineering🔒
  4. 4📖❓Model Evaluation🔒
  5. 5📖The AI Tools Ecosystem🔒

From Problem to Model

0/2 lessons
  1. 6📖❓Defining the ML Problem🔒
  2. 7📖❓Train / Validation / Test Split🔒

Core Techniques

0/3 lessons
  1. 8📖❓Linear Regression🔒
  2. 9📖🔬Logistic Regression🔒
  3. 10📖🔬Decision Trees and Random Forests🔒

Knowing When It Works

0/3 lessons
  1. 11📖❓Classification Metrics🔒
  2. 12📖🔬Cross-Validation🔒
  3. 13📖❓Model Cards and Documentation🔒

From Notebook to App

0/3 lessons
  1. 14📖🔬Saving and Loading a Model🔒
  2. 15📖❓Input Validation for ML Services🔒
  3. 16📖❓Monitoring Model Drift🔒