Machine Learning Essentials
Build a solid mathematical foundation for neural networks by learning the core concepts behind Machine Learning and how they connect.
What you’ll learn
- Implement and interpret linear and logistic regression from scratch.
- Use maximum likelihood estimation (MLE) to understand and guide learning.
- Decide between supervised and unsupervised methods based on data and goals.
- See how MLE maps directly to losses: MSE for linear, cross-entropy for logistic.
Hands-on application
- Work with real datasets: clean, split, and structure them correctly.
- Select appropriate objective functions for regression vs. classification.
- Train with gradient descent and evaluate using RMSE, accuracy, precision/recall, ROC-AUC.
Prerequisites
- Basic calculus (derivatives) and intro statistics (probability distributions).
- Some programming experience (Python preferred).
Who it’s for
- Developers new to ML who want a solid mathematical base before deep learning.
- Builders who learn best by starting from first principles.
Format
- Duration: ~4–6 hours, fully self-paced.
- Structure: 6 bite-sized modules → short lesson, worked example, then a notebook to implement.