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ML Foundationsfirst principles.

MACHINE LEARNING • CONCEPTS FIRST.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for ML Foundations (Machine Learning).

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Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

ML Foundations (Machine Learning)

1. The Machine Learning Landscape

1.1 What Is Machine Learning?
1.2 Types of ML Systems
1.3 Batch vs Online Learning
1.4 Instance-Based vs Model-Based Learning

2. Main Challenges of ML

2.1 Bad Data
2.2 Overfitting & Underfitting
2.3 The Bias–Variance Trade-off

3. Testing and Validating

3.1 Train, Validation & Test Sets
3.2 Cross-Validation
3.3 Hyperparameter Tuning & Model Selection

4. An End to End ML Project

4.1 Frame the Problem & Pick a Metric
4.2 Get the Data & Create a Test Set
4.3 Explore & Visualize
4.4 Prepare the Data
4.5 Feature Engineering
4.6 Train, Fine-Tune & Evaluate
4.7 Launch, Monitor & Maintain
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

scienceOpen Interactive Laboratories→Board MCQs & Past Papers