Next Steps in Machine Learning

Well done! You have seen the full ML workflow: understand the data, split it, train a model, predict, and measure accuracy — with real code running in your browser.

What you have learned

• What machine learning is and its three main types
• Describing data with mean, median, mode, spread, and percentiles
• Visualising relationships with scatter plots
• Predicting numbers with linear and polynomial regression
• Predicting categories with KNN and decision trees
• Finding groups with K-Means clustering
• Scoring a model with accuracy

Where to go next

1. Practice with real datasets — try the classic Iris or Titanic datasets
2. Learn Pandas — the tool for loading and cleaning real data (a great next course)
3. Try Random Forests and Logistic Regression — reliable, widely-used models
4. Explore neural networks — the foundation of deep learning and modern AI

Key terms to remember

Feature (X), Label (y), train/test split, fit, predict, overfitting, accuracy. These words appear in almost every ML tutorial you will read next.

💡 Keep going: the best way to learn ML is to keep building small projects. Pick something you are curious about and try to predict it!