Intermediate 16 planned topics AI
Introduction to Machine Learning
Learn machine learning types, train/test splits, and classic models. Free ML introduction.
Learning Outcomes
- Name the main ML problem types
- Split data to avoid leaking the test set
- Explain overfitting in plain language
- Pick accuracy versus F1 at a high level
Overview
Machine learning finds patterns from examples instead of hard-coded rules. Supervised, unsupervised, and reinforcement are the usual buckets.
This introduction covers the train/validation/test split and overfitting.
You will later meet regression, trees, and metrics.
What this course will cover
- 1. Problem types
- 2. Data split
- 3. Regression
- 4. Classification
- 5. Metrics
- 6. Pipelines
Start a complete tutorial now
Full 6-level lessons are live for C, C++, Java, Python, and JavaScript. Use them while the remaining Machine Learning lessons are added.