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2022 Machine Learning A to Z : 5 Machine Learning Projects

#artificialintelligence

Evaluation metrics to analyze the performance of models Industry relevance of linear and logistic regression Mathematics behind KNN, SVM and Naive Bayes algorithms Implementation of KNN, SVM and Naive Bayes using sklearn Attribute selection methods- Gini Index and Entropy Mathematics behind Decision trees and random forest Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost Different Algorithms for Clustering Different methods to deal with imbalanced data Correlation Filtering Content and Collaborative based filtering Singular Value Decomposition Different algorithms used for Time Series forecasting Hands on Real-World examples. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning.


The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial

arXiv.org Machine Learning

In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and generalization errors of the model for both training and validation/test instances where we make use of the Stein's Unbiased Risk Estimator (SURE). We define overfitting, underfitting, and generalization using the obtained true and generalization errors. We introduce cross validation and two well-known examples which are $K$-fold and leave-one-out cross validations. We briefly introduce generalized cross validation and then move on to regularization where we use the SURE again. We work on both $\ell_2$ and $\ell_1$ norm regularizations. Then, we show that bootstrap aggregating (bagging) reduces the variance of estimation. Boosting, specifically AdaBoost, is introduced and it is explained as both an additive model and a maximum margin model, i.e., Support Vector Machine (SVM). The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting. As examples of regularization, the theory of ridge and lasso regressions, weight decay, noise injection to input/weights, and early stopping are explained. Random forest, dropout, histogram of oriented gradients, and single shot multi-box detector are explained as examples of bagging in machine learning and computer vision. Finally, boosting tree and SVM models are mentioned as examples of boosting.