EulerFoldMachine Learning from Scratch
“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.”
Week 1: ML Fundamentals & Algorithms from Scratch
By the end of this module you will be able to implement fundamental machine learning algorithms like Linear Regression, Logistic Regression, and K-Means clustering using only NumPy, demonstrating a deep understanding of their underlying mathematical principles.
Week 2: Scaling ML, Neural Networks & Real-World Applications
By the end of this module you will be able to apply scikit-learn for common ML tasks, build a basic neural network with PyTorch, and implement robust ML pipelines including data preprocessing, proper train/test splits, cross-validation, and model evaluation on real-world datasets.
References
Week 1: Week 1: ML Fundamentals & Algorithms from Scratch
Machine Learning Tutorial - GeeksforGeeks
geeksforgeeks.orgMachine Learning from Scratch - eulerfold.com
eulerfold.commicrosoft/ML-For-Beginners - GitHubML Algorithms from Scratch - Educational Python ...Machine Learning Full Course From Scratch 2026 | IntellipaatMachine Learning | Google for DevelopersMachine Learning from Scratch - Udemy
github.comWeek 2: Week 2: Scaling ML, Neural Networks & Real-World Applications
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