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Machine Learning from Scratch

2 weeks
15 Learners
•
Machine Learning from Scratch

Objective

“Master the full ML pipeline , from linear algebra, calculus, and probability fundamentals to implementing regression, classification, clustering, and neural networks from scratch in NumPy, then scaling to scikit-learn, PyTorch, and real-world datasets with proper train/val/test splits, cross-validation, and model evaluation metrics.”

Topics

1.1
Python for Data Science Essentials
1.2
Linear Algebra Essentials
1.3
Calculus Essentials
1.4
Probability & Statistics Basics
1.5
Linear Regression from Scratch
1.6
Logistic Regression from Scratch
1.7
K-Means Clustering from Scratch

Study Resources

→NumPy Quickstart Tutorial→Linear Algebra (Khan Academy)→Gradient Descent, Clearly Explained!!! (StatQuest)
01

Learn

Watch curated videos and read study resources

02

Practice

Practice what you learned

03

Build Projects

Build projects using your new gained knowledge

04

Submit & Verify

Submit your project and get verified by our system

References

01NumPy Quickstart Tutorial
02Linear Algebra (Khan Academy)
03Gradient Descent, Clearly Explained!!! (StatQuest)
04Scikit-learn User Guide
05PyTorch Official Tutorials
06PyTorch for Deep Learning - Full Course (freeCodeCamp)

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