Topics I : (Inside Learn ML Folder)
- Supervised Learning
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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)
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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)
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Both Regression and Classification
- KNN Classifier
- Naive Bayes Classifier( more used in Classification ,therefore added in Classification Section )
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