Machine Learning

"Welcome to the course Introduction Setting up R Studio and R crash course 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 Creating Barplots in R Creating Histograms in R Basics of Statistics Types of Data Types of Statistics Describing the data graphically Measures of Centers Measures of Dispersion Intorduction to Machine Learning Introduction to Machine Learning Building a Machine Learning Model Data Preprocessing for Regression Analysis Gathering Business Knowledge Data Exploration The Data and the Data Dictionary 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 Linear Regression Model 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 Regression models other than OLS 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 Classification Models: Data Preparation 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 The Three classification models Three Classifiers and the problem statement Why can’t we use Linear Regression? Logistic 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 Linear Discriminant Analysis in R K-Nearest Neighbors Test-Train Split Test-Train Split in R K-Nearest Neighbors classifier K-Nearest Neighbors in R Comparing results from 3 models Understanding the results of classification models Summary of the three models Simple Decision Trees 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 Simple Classification Tree Classification Trees The Data set for Classification problem Building a classification Tree in R Advantages and Disadvantages of Decision Trees Ensemble technique 1 - Bagging Bagging Bagging in R Ensemble technique 2 - Random Forest Random Forest technique Random Forest in R Ensemble technique 3 - GBM, AdaBoost and XGBoost Boosting techniques Gradient Boosting in R AdaBoosting in R XGBoosting in R Maximum Margin Classifier Content flow The Concept of a Hyperplane Maximum Margin Classifier Limitations of Maximum Margin Classifier Support Vector Classifier Support Vector classifiers Limitations of Support Vector Classifiers Support Vector Machines Kernel Based Support Vector Machines Creating Support Vector Machine Model in R The Data set for 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"