Machine Learning Masterclass

Introduction

Installing R and R studio

Basics of R and R studio

Packages in R

Inputting data part 1: Inbuilt datasets of R

Inputting data part 2: Manual data entry

Inputting data part 3: Importing from CSV or Text files

Creating Barplots in R

Creating Histograms in R

Types of Data

Types of Statistics

Describing the data graphically

Measures of Centers

Measures of Dispersion

Introduction to Machine Learning

Building a Machine Learning Model

Gathering Business Knowledge

Data Exploration

The Data and the Data Dictionary

Importing the dataset into R

Univariate Analysis and EDD

EDD in R

Outlier Treatment

Outlier Treatment in R

Missing Value imputation

Missing Value imputation in R

Seasonality in Data

Bi-variate Analysis and Variable Transformation

Variable transformation in R

Non Usable Variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in R

Correlation Matrix and cause-effect relationship

Correlation Matrix in R

The problem statement

Basic equations and Ordinary Least Squared (OLS) method

Assessing Accuracy of predicted coefficients

Assessing Model Accuracy – RSE and R squared

Simple Linear Regression in R

Multiple Linear Regression

The F – statistic

Interpreting result for categorical Variable

Multiple Linear Regression in R

Test-Train split

Bias Variance trade-off

Test-Train Split in R

Linear models other than OLS

Subset Selection techniques

Subset selection in R

Shrinkage methods – Ridge Regression and The Lasso

Ridge regression and Lasso in R

The Data and the Data Dictionary

Importing the dataset into R

EDD in R

Outlier Treatment in R

Missing Value imputation in R

Variable transformation in R

Dummy variable creation in R

Three Classifiers and the problem statement

Why can’t we use Linear Regression?

Logistic Regression

Training a Simple Logistic model in R

Results of Simple Logistic Regression

Logistic with multiple predictors

Training multiple predictor Logistic model in R

Confusion Matrix

Evaluating Model performance

Predicting probabilities, assigning classes and making Confusion Matrix in R

Linear Discriminant Analysis

Linear Discriminant Analysis in R

Test-Train Split

Test-Train Split in R

K-Nearest Neighbors classifier

K-Nearest Neighbors in R

Understanding the results of classification models

Summary of the three models

Basics of Decision Trees

Understanding a Regression Tree

The stopping criteria for controlling tree growth

The Data set for this part

Importing the Data set into R

Splitting Data into Test and Train Set in R

Building a Regression Tree in R

Pruning a tree

Pruning a Tree in R

Classification Trees

The Data set for Classification problem

Building a classification Tree in R

Advantages and Disadvantages of Decision Trees

Bagging

Bagging in R

Random Forest technique

Random Forest in R

Boosting techniques

Gradient Boosting in R

AdaBoosting in R

XGBoosting in R

Content flow

The Concept of a Hyperplane

Maximum Margin Classifier

Limitations of Maximum Margin Classifier

Support Vector classifiers

Limitations of Support Vector Classifiers

Kernel Based Support Vector Machines

The Data set for the Classification problem

Importing Data into R

Test-Train Split

Classification SVM model using Linear Kernel

Hyperparameter Tuning for Linear Kernel

Polynomial Kernel with Hyperparameter Tuning

Radial Kernel with Hyperparameter Tuning

The Data set for the Regression problem

SVM based Regression Model in R