Machine learning with R

Course Overview

Introduction– Its algorithms, like linear regression, logistic regression, decision tree, random forest, SVM, and hierarchic clustering techniques. and its varied applications. you'll additionally learn R programming well along side statistic analysis in R.

About python data science certification– You will receive a Course Completion Certificate from XcelitPto upon finishing the f Machine Learning with R program. . As shortly because the certificate is unbarred, you'll receive a mail with a link to your mail address. Click the link to look at and transfer your certificate. you'll even add the certificate to your resume and share it on social media platforms. –

Key features

Statistical Learning
R for Machine Learning
Fundamentals of Machine Learning
optimization Techniques
Machine Learning Algorithms
Ensemble Learning
spatiality Reduction

Pre-requisites

There are not any prerequisites to learn Machine Learning with R. However, it's advised that learners have a basic understanding of mathematics, statistics and programming.

Course Syllabus

Machine Learning with R Course Syllabus

What is supervised learning
Algorithms in Supervised learning
Steps in Supervised learning

Regression & Classification

Regressionvs classification
Computation of co-relation coefficient and Analysis
Performance and accuracy measurement of a Model
NaiveBaye’s classifier, Model Training, Validation and Testing
Ordinary Least squares, Variable selection
R-Square coefficient and RMSE as a strength of model, Prediction and confidence interval determination and application
Proviso of Regression, Dummy variables, Types of Regression: Linear and Logistic( Simple and multiple)
Sum of least squares, ROC and AUC curves, Homoscedasticity and Heteroscedasticity, Multicollinearity and vif, Confusion matrix
Techniques to improve accuracy and performance of regression models
Assignment

Decision Trees and Random Forest Test

Introduction to Decision tree Algorithms and it’s applications
Classification and regression trees-CART models,ID3,C4.5, CHAID analysis
Building Decision Trees using R, Decision nodes and leaf nodes
Variable Selection, Parent and child nodes branching
Stopping Criterion, Tree pruning, Depth of a tree, Overfitting
Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
Regression using decision tree
Interpretation of a decision tree using If-else
Pros and cons of a decision tree
Introduction to Random forest test and it’s applications, Why Random forest test?
Tree bagging, Models and algorithms in Random Forest test
Training Data set, Tree grouping and decision making on majority voting
Boosting algorithms-Gradient Boosting, Adaptive boosting-Adaboost , Xgboost ( Advanced)
Accuracy estimation using cross validation

Machine Learning with R

Course Overview

Course Syllabus

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Machine Learning with R

Specifications