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parameter-estimation

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OLS. R and Python. In this project, we study fundamental concepts of Supervised ML models, such as Regression Analysis: Coefficient of Model Adjustment (R²), Parameters Estimation ,Statistical Significance of the Model (F test, T test) ,Multiple Regression , Qualitative Explanatory Variables (X) , heteroscedasticity and etc.

  • Updated Dec 13, 2023
  • Jupyter Notebook

Assignments completed for my Machine Learning course: Topics include probability and statistics proofs, MLE/MAP parameter estimation, EM Algorithm, Bayes Theorem implementations, gradient descent methods, Neural Networks and Deep Learning.

  • Updated Jan 28, 2023
  • Python

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