Machine Learning Course – Complete Syllabus
Below is the complete syllabus of our Machine Learning course.
Click any chapter to open the full lesson.
Chapter 1: Machine Learning Fundamentals
Introduction to ML, types, real-world applications.
Chapter 2: Supervised vs Unsupervised Learning
Differences, examples, and use cases.
Chapter 3: Overview of ML Algorithms
Regression, classification, clustering algorithms.
Chapter 4: Data Preprocessing & Scaling
Cleaning data, encoding, feature scaling, outliers.
Chapter 5: Train-Test Split & Cross-Validation
Testing models, K-fold CV, validation sets.
Chapter 6: Evaluation Metrics
Accuracy, Precision, Recall, F1 Score, Confusion Matrix.
Chapter 7: Bias–Variance Tradeoff
Underfitting, overfitting, L1/L2 regularization.
More advanced chapters will be added soon.
