Machine learning with R (RF, Adabost.M1, DT, NB, LR, NN)
3.3 (8 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,562 students enrolled

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

RF, Adabost.M1, DecisionTree, Logistic Regression , Naive Bayes, Neural Network, CNN, K-mean , Linear regress
3.3 (8 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,562 students enrolled
Created by Modeste Atsague
Last updated 5/2018
English
Current price: $23.99 Original price: $34.99 Discount: 31% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 3 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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 .
Course content
Expand 15 lectures 02:48:47
+ Introduction
15 lectures 02:48:47
General Concepts (Topics to be cover )
02:23
Set working directory , import data , missing values detection
18:41
Random Forest
32:51
Adabost.M1
19:20
Decision Tree
10:24
Logistic Regression (LR)
09:39
Naive Bayes
10:33
Training and Prediction with Neural Network (NN)
05:40
Training and Prediction with Convolutional neural Network (KNN)
04:49
How to combine models to predict (Ensemble classifier )
10:56
Missing values treatment , linear regression (Best Parameters for prediction)
19:06
k mean clustering
11:05
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
  1. Training and prediction using the Random Forest model , prediction accuracy, Confusion matrix  and confidence interval 
  2. Training and prediction using the  Adabost.M1 model , prediction accuracy, Confusion matrix and confidence interval 
  3. Training and prediction using the Decision Tree model , prediction accuracy, Confusion matrix and confidence interval
  4. Training and prediction using the logistic regression model, prediction accuracy, confusion matrix and confidence interval 
  5. Training and prediction using the Naive Bayes model, prediction accuracy, confusion matrix and confidence interval 
  6. Training and prediction using the Neural Network model , prediction  accuracy, confusion matrix and confidence interval 
  7. 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 
Who this course is for:
  • If you are interested on predictive analytic , then this course is a right fit for you.