# Machine learning with R (RF, Adabost.M1, DT, NB, LR, NN)

*Get this course for free.*

*Machine learning with R*(*RF*,*Adabost.M1*,*DT*,*NB*,*LR*,*NN*) download - Udemy Coupon - 100% discount.
3.5
(7 ratings)

2,560 students enrolled

Created by
Modeste Atsague

Last updated 5/2018

English

30-Day Money-Back Guarantee

This course includes

- 3 hours on-demand video
- Full lifetime access
- Access on mobile and TV

- Certificate of Completion

Training 5 or more people?

Get your team access to 3,500+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn

- At the end this course, A student will be able to do the following: For continuous data , you will be able to Train a linear regression model , select the best linear model for a given data and predict. For categorical data (Binary classification task ), you will be able to train models such as logistic regression (LR), Decision Tree (DT), Neural Network (NN), Convolutional Neural Network (CNN or ConVnet) , AdaBoost.M1, Random Forest (RF) and Naïve Bayes (NB) . You will be able to combine models to better your prediction. For clustering task, out of this class, a student will be able to implement the K-mean clustering which is the widely used clustering algorithm .

Requirements

- No Prior programing knowledge is required. However a minimum knowledge of any programming and basic statistics is a plus

Description

*How to download and install R***How to set your working directory import your data and detect rows containing missing values****For binary classification**

*Training and prediction using the***Random Forest**model , prediction accuracy, Confusion matrix and confidence interval*Training and prediction using the***Adabost.M1**model , prediction accuracy, Confusion matrix and confidence interval*Training and prediction using the***Decision Tree**model , prediction accuracy, Confusion matrix and confidence interval*Training and prediction using the***logistic regression**model, prediction accuracy, confusion matrix and confidence interval*Training and prediction using the***Naive Bayes**model, prediction accuracy, confusion matrix and confidence interval*Training and prediction using the***Neural Network**model , prediction accuracy, confusion matrix and confidence interval*Training and prediction using the***Convolutional neural network (KNN)**, prediction accuracy, confusion matrix and confidence interval

*How to combine models to predict**Missing values treatment ,variables selection and prediction using a linear regression model**K mean Clustering*

## Post a Comment for "Machine learning with R (RF, Adabost.M1, DT, NB, LR, NN) "