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

Deal Score+6

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
  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

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