Machine Learning classes

 

Machine Learning classes

Andrew Ng’s Machine Learning (Stanford)	2017	~9 weeks	Supervised/unsupervised learning	Octave, Scikit-learn	Outdated but covers foundational algorithms (e.g., k-means, SVM).
Deep Learning Specialization (Andrew Ng)	2023	~5 months	Advanced DL architectures	TensorFlow	Covers CNNs, RNNs, and modern optimizers (Adam).
DeepMind’s Technical Writing (ML Basics)	2021	~10 hrs	Writing for AI papers	LaTeX, Markdown	Useful for documenting algorithms (e.g., white papers).
Fast.ai: Practical Deep Learning	2018	~3 weeks	Hands-on ML with real datasets	PyTorch	Best for quick deployment; emphasizes practicality over theory.
Google’s Machine Learning Crash Course	2018	~5 hrs	Beginner ML workflows	TensorFlow	Good for quick basics but lacks depth.
MIT’s Introduction to Deep Learning	2023	~4 weeks	PyTorch/TensorFlow fundamentals	PyTorch, TensorFlow	Updated for modern architectures (e.g., transformers).
Neural networks by 3Blue1Brown	2025				Great starter, best explanations.
Udacity’s Machine Learning Engineer Nanodegree	2017	~3 months	Production-grade ML systems	TensorFlow, TFX	Covers MLOps (e.g., pipelines) but outdated for PyTorch.