MastAI ki paathSHALA
| Assignment | Content |
|---|---|
| 1 | Getting started : Python data structure, Loops, Classes, Linear Algebra |
| 2 | Basic data understanding: Data science, Central tendency, Plots, Cumulative distribution |
| 3 | Improving plots: :Different types of plots, How to customize plots |
| 4 | Basic statistics : Maximum likelihood estimation, sufficient statistics, null hypothesis testing, t-test, Wilcoxon rank test |
| 5 | Introduction to ML : Machine learning problems, parameter vs. hyperparameter, overfitting, training, validation, testing, cross-validation, regularization |
| 6 | Decision Trees : Definition of a decision tree, metrics of impurity, greedy algorithm to split a node, tree depth and pruning, ensemble of trees (random forest) |
| 7 | Bayesian decision theory : Bayes rule: Prior, likelihood, posterior, evidence, Gaussian density, sufficient statistics, maximum likelihood derivation for mean and covariance |
| 8 | Linear models : linear regression and its analytical solution, loss function, gradient descent and learning rate, logistic regression and its cost, SVM: hinge loss with L2 penalty |