Machine learning with R (RF, Adabost.M1, DT, NB, LR, NN) - Udemy course 100% OFF

Toward the end this course, An understudy will almost certainly do the accompanying: For ceaseless information , you will most likely Train a direct relapse model , select the best straight model for a given information and foresee. For unmitigated information (Binary arrangement task ), you will almost certainly train models, for example, calculated relapse (LR), Decision Tree (DT), Neural Network (NN), Convolutional Neural Network (CNN or ConVnet) , AdaBoost.M1, Random Forest (RF) and Naïve Bayes (NB) . You will almost certainly join models to better your expectation. For bunching task, out of this class, an understudy will most likely actualize the K-mean grouping which is the generally utilized bunching calculation..

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