Machine Learning in R - nsarrows

Machine Learning in R

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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

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