 Machine Learning in R - nsarrows

# Machine Learning in R

Category:

## Description

This is what you will be learning in the course:

• Introduction to R and RStudio
• Inferential Statistics
• Probability
• Statistics (Central Tendency, Spread of Data, Data Distributions, ANOVA, CHI SQUARE Test)
• Univariate Analysis
• Bivariate Analysis
• Hypothesis Testing (t-test, z-test, p-value)
• Quiz
• Linear Regression
• Understanding the concept Linear Regression
• Maths behind Linear Regression
• Lasso Regression and Ridge Regression
• Case Study 1 (Theory + Practicals + Reporting)
• Case Study 2 (Theory + Practicals + Reporting)
• Quiz
• Logistic Regression
• Understanding the concept of Logistic Regression
• Maths behind Logistic Regression
• AUC and ROC Curves, Confusion Matrix (Accuracy, Sensitivity and Specificity, Precision and Recall)
• Cutoff Methods in Logistic Regression (Min Distance method, KS Method, Lift Method, F1 Beta Method)
• Case Study 1 (Theory + Practicals + Reporting)
• Case Study 2 (Theory + Practicals + Reporting)
• Quiz
• Capstone Project 1 for Certification
• Clustering
• Hierarchical Clustering (Maths + Case Study + Practicals + Reporting)
• K-Means Clustering (Maths + Case Study + Practicals + Reporting)
• DB Scan (Maths + Case Study + Practicals + Reporting)
• Quiz
• Decision Trees
• Maths behind decision trees – Information Gain, Remainder,  Loss
• C 4.5 (Maths + Case Study + Practicals + Reporting)
• Random Forest (Maths + Case Study + Practicals + Reporting)
• Quiz
• Time Series
• Understanding Time Series and its concepts
• Exponential Smoothening 1 (Maths + Case Study + Practicals + Reporting)
• Exponential Smoothening 2 (Maths + Case Study + Practicals + Reporting)
• Exponential Smoothening 3 (Maths + Case Study + Practicals + Reporting)
• ARIMA (Maths + Case Study + Practicals + Reporting)
• Quiz
• Introduction to Boosting, Bagging and Cross Validation
• Master Quiz for Certification
• Capstone Project 2 for Certification

## Reviews

There are no reviews yet.