Course content
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Mathematical Foundationsx Lessons
- Where Did The Assumptions of Linear Regression Originate From?
- The Probabilistic Origin of Regularization
- Why Do We Use Sigmoid in Logistic Regression?
- Why Do We Use log-loss To Train Logistic Regression?
- Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?
- Why Bagging is So Ridiculously Effective At Variance Reduction?
- A Mathematical Deep Dive Into the Curse of Dimensionality
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Regression & Classificationx Lessons
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Clustering & Dimensionality Reductionx Lessons
- Formulating the Principal Component Analysis (PCA) Algorithm From Scratch
- Formulating and Implementing the t-SNE Algorithm From Scratch
- DBSCAN++: The Faster and Scalable Alternative to DBSCAN Clustering
- HDBSCAN: The Supercharged Version of DBSCAN (An Algorithmic Deep Dive)
- Gaussian Mixture Models (GMMs)
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Deep Learning Architectures & Trainingx Lessons
- Why is ReLU a Non-Linear Activation Function?
- A Detailed and Beginner-Friendly Introduction to PyTorch Lightning: The Supercharged PyTorch
- 15 Ways to Optimize Neural Network Training (With Implementation)
- A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KAN)
- Implementing KANs From Scratch Using PyTorch
- Federated Learning: A Critical Step Towards Privacy-Preserving ML
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Model Reliability & Interpretabilityx Lessons
Published on Jun 1, 2025