The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy for learners to navigate and practice. The exercises serve as practical, hands-on reinforcement of theoretical concepts taught in the course. This collection is valuable for students and practitioners who want to strengthen their skills in machine learning through coding exercises.

Features

  • Contains programming exercises from Stanford’s Machine Learning course
  • Implements algorithms in Python and MATLAB/Octave
  • Covers supervised learning methods including regression and classification
  • Includes unsupervised learning methods such as clustering and PCA
  • Provides neural network training and optimization examples
  • Features recommender systems and anomaly detection exercises

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Machine Learning

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Additional Project Details

Programming Language

MATLAB, Python, Unix Shell

Related Categories

Unix Shell Machine Learning Software, MATLAB Machine Learning Software, Python Machine Learning Software

Registered

20 hours ago