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batch-gradient-descent

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machine-learning

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

  • Updated Dec 16, 2022
  • Jupyter Notebook

Advanced Twitter sentiment analysis pipeline using Apache Spark for distributed data processing, featuring TF-IDF–based feature engineering and stochastic gradient-descent classification for scalable, real-time sentiment insights.

  • Updated May 7, 2025
  • Jupyter Notebook

Compilation of different ML algorithms implemented from scratch (and optimized extensively) for the courses COL774: Machine Learning (Spring 2020) & COL772: Natural Language Processing (Fall 2020)

  • Updated Jan 27, 2021
  • Python

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.

  • Updated Jul 22, 2025
  • Jupyter Notebook

Two mountaineers search for the global minimum of a cost function using different approaches. One represents Stochastic Gradient Descent, taking small, random steps, while the other follows Batch Gradient Descent, making precise moves after full evaluation. This analogy illustrates key optimization strategies in machine learning.

  • Updated Apr 12, 2025
  • Jupyter Notebook

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