Advanced Machine Learning with R Studio by Start-Tech Academy

Advanced Machine Learning with R Studio

Covers classification models such as Logistic regression, LDA, KNN, advanced ML models such as Decision Trees and SVMs

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Contents

Basics of Statistics
Types of Data
5 mins
Types of Statistics
3 mins
Describing data Graphically
12 mins
Measures of Centers
8 mins
Measures of Dispersion
5 mins
Setting up R Studio and R crash course
Installing R and R studio
6 mins
Basics of R and R studio
11 mins
Packages in R
11 mins
Inputting data part 1 - Inbuilt datasets of R
5 mins
Inputting data part 2 - Manual data entry
4 mins
Inputting data part 3 - Importing from CSV or Text files
7 mins
Creating Barplots in R
14 mins
Creating Histograms in R
7 mins
Introduction to Machine Learning
Introduction to Machine Learning
9 mins
Building a Machine Learning model
17 mins
Data Preprocessing
Data Exploration
4 mins
Gathering Business Knowledge
4 mins
The Dataset and the Data Dictionary
9 mins
Importing the dataset into R
3 mins
Univariate analysis and EDD
4 mins
EDD in R
12 mins
Outlier Treatment
5 mins
Outlier Treatment in R
5 mins
Missing Value Imputation
4 mins
Missing Value imputation in R
4 mins
Seasonality in Data
4 mins
Variable Transformation
2 mins
Variable transformation in R
7 mins
Dummy variable creation: Handling qualitative data
5 mins
Dummy variable creation in R
6 mins
Classification Models: Logistic, LDA & KNN
Three Classifiers and the problem statement
4 mins
Why can't we use Linear Regression?
5 mins
Logistic Regression
8 mins
Training a Simple Logistic model in R
4 mins
Result of Simple Logistic Regression
6 mins
Logistic with multiple predictors
3 mins
Training multiple predictor Logistic model in R
2 mins
Confusion Matrix
4 mins
Evaluating performance of model
8 mins
Predicting probabilities assigning classes and making Confusion Matrix
7 mins
Linear Discriminant Analysis
10 mins
Linear Discriminant Analysis in R
10 mins
Test-Train Split
10 mins
Test-Train Split in R
10 mins
K-Nearest Neighbors classifier
9 mins
K-Nearest Neighbors in R
9 mins
Understanding the results of classification models
7 mins
Summary of the three models
5 mins
Decision Trees
Basics of decision trees
11 mins
Understanding a Regression Tree
11 mins
The stopping criteria for controlling tree growth
4 mins
The Data set for the Course
3 mins
Importing the Data set into R
7 mins
Splitting Data into Test and Train Set in R
6 mins
Building a Regression Tree in R
15 mins
Pruning a tree
5 mins
Pruning a Tree in R
10 mins
Classification tree
7 mins
The Data set for Classification problem
2 mins
Building a classification Tree in R
9 mins
Advantages and Disadvantages of Decision Trees
2 mins
Ensemble technique 1 - Bagging
7 mins
Bagging in R
7 mins
Ensemble technique 2 - Random Forests
4 mins
Random Forest in R
4 mins
Ensemble technique 3 - Boosting
8 mins
Gradient Boosting in R
8 mins
AdaBoosting in R
10 mins
XGBoosting in R
17 mins
Support Vector Machines
Topic flow
2 mins
The Concept of a Hyperplane
5 mins
Maximum Margin Classifier
4 mins
Limitations of Maximum Margin Classifier
3 mins
Support Vector classifiers
10 mins
Limitations of Support Vector Classifiers
2 mins
Kernel Based Support Vector Machines
7 mins
The Data set for the Classification problem
2 mins
Importing Data into R
8 mins
Test-Train Split
6 mins
Classification SVM model using Linear Kernel
17 mins
Hyperparameter Tuning for Linear Kernel
7 mins
Polynomial Kernel with Hyperparameter Tuning
11 mins
Radial Kernel with Hyperparameter Tuning
7 mins
The Data set for the Regression problem
3 mins
SVM based Regression Model in R
12 mins