Machine Learning

Topics I : (Inside Learn ML Folder)

• Supervised Learning

  • Regression

    • Basic Data Generation
    • Sklearn Boston and Digits Datasets
    • Univariate Linear Regression
    • Multivarate Linear Regression
    • Overfitting VS Underfitting
    • Polynomial Regression
    • Stochastic vs Mini-Batch Gradient Descent
    • Closed Normal Form of LR
    • Locally weighted Regression
    • Assignment 1 -> Hardwork Pays OFF(Coding Blocks)
    • Assignment 2 -> Air Quality Prediction(Kaggle)

  • Classification

    • Logistic Regression(Theory + Code)
    • Naive Bayes (Theory + Code)
    • Assignment 3 - Chemicals (Coding Blocks)
    • Types of Naive Bayes(Multinomial,Multivariate-Bernoulli ,Gaussian NB)
    • Multinomial Naive Bayes for Text Classification
    • Applying Multinomial NB on MNIST data Sklearn
    • Assignment 4 - IMDB Movie Rating Prediction based on review(Coding Blocks)

  • Both Regression and Classification

    • KNN Classifier
    • Naive Bayes Classifier( more used in Classification ,therefore added in Classification Section )

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