Course Curriculum
| Part 1: Introduction to R Programming | |||
| Session 1 - Introduction to R | |||
| Handout – Introduction to R | 00:00:00 | ||
| 1.1 Getting Started in R | FREE | 00:00:00 | |
| 1.2 R Environment | FREE | 00:00:00 | |
| 1.3 R Packages | 00:00:00 | ||
| 1.4 R Data Types Vectors | 00:00:00 | ||
| 1.5 R Dataframes | 00:00:00 | ||
| 1.6 List in R | 00:00:00 | ||
| 1.7 Factor and Matrices | 00:00:00 | ||
| 1.8 R History and Scripts | 00:00:00 | ||
| 1.9 R Functions | 00:00:00 | ||
| 1.10 Errors in R | 00:00:00 | ||
| R quiz unit 1 | Unlimited | ||
| Session 2 - Data Handling in R | |||
| Handout – Data Handling in R | 00:00:00 | ||
| 2.1 Data handling introduction | FREE | 00:00:00 | |
| 2.2 Importing the Datasets | FREE | 00:00:00 | |
| 2.3 Checklist | 00:00:00 | ||
| 2.4 Subsetting the Data | 00:00:00 | ||
| 2.5 Subsetting Variable Condition | 00:00:00 | ||
| 2.6 Calculated Fields _ ifelse | 00:00:00 | ||
| 2.7 Sorting and Duplicates | 00:00:00 | ||
| 2.8 Joining and Merging | 00:00:00 | ||
| 2.9 Exporting the Data | 00:00:00 | ||
| R Quiz Unit 2 | Unlimited | ||
| Session 3 - Basic Descriptive Statistics & Reporting | |||
| Handout – Basic Statistics, Plots and Reporting in R | 00:00:00 | ||
| 3.1 Introduction and Sampling | FREE | 00:00:00 | |
| 3.2 Descriptive Statistics | FREE | 00:00:00 | |
| 3.3 Percentiles and Quartiles | 00:00:00 | ||
| 3.4 Box Plots | 00:00:00 | ||
| 3.5 Creating Graphs and Conclusion | 00:00:00 | ||
| R Quiz Unit 3 | Unlimited | ||
| Session 4 - Data Cleaning and Treatement | |||
| Handout – Data Cleaning and Treatment in R | 00:00:00 | ||
| 4.1 Data Cleaning Intro and Model Building Cycle | FREE | 00:00:00 | |
| 4.2 Model Building Cycle | FREE | 00:00:00 | |
| 4.3 Data Cleaning Case Study | 00:00:00 | ||
| 4.4 CS lab Step1 Basic Content of Dataset | 00:00:00 | ||
| 4.5 Variable Level Exploration Catagorical | 00:00:00 | ||
| 4.6 Reading Data Dictionary | 00:00:00 | ||
| 4.7 Step2 lab Catagorical Variable Exploration | 00:00:00 | ||
| 4.8 Step3 lab Variable level Exploration – Continuous | 00:00:00 | ||
| 4.9 Data Cleaning and Treatments | 00:00:00 | ||
| 4.10 Step 4 Treatment – Scenario1 | 00:00:00 | ||
| 4.11 Step 4 Treatment – Scenario 2 | 00:00:00 | ||
| 4.12 Data Cleaning – Scenario 3 | 00:00:00 | ||
| 4.13 Some Other Variables | 00:00:00 | ||
| 4.14 Conclusion | 00:00:00 | ||
| R Quiz Unit 4 | Unlimited | ||
| Part 2: Machine Learning using R | |||
| Session 1 - Regression Analysis | |||
| Handout – Regression Analysis | 00:00:00 | ||
| 1.1 Introduction and Corelation | FREE | 00:00:00 | |
| 1.2 LAB Corelation Calculation in R | FREE | 00:00:00 | |
| 1.3 Beyond Pearson Corelation | 00:00:00 | ||
| 1.4 From Corelation to Regression | 00:00:00 | ||
| 1.5 Regression Line Fitting in R | 00:00:00 | ||
| 1.6 R Squared | 00:00:00 | ||
| 1.7 Multiple Regression | 00:00:00 | ||
| 1.8 Adjusted R Squared | 00:00:00 | ||
| 1.9 Issue with Multiple Regression | 00:00:00 | ||
| 1.10 Multicollinearity | 00:00:00 | ||
| 1.11 Regression Conclusion | 00:00:00 | ||
| Regression Quiz | Unlimited | ||
| Session 2 - Logistic Regression | |||
| Handout – Logistic Regression in R | 00:00:00 | ||
| 2.1 Need of Non-Linear Regression | FREE | 00:00:00 | |
| 2.2 Logistic Function and Line | FREE | 00:00:00 | |
| 2.3 Multiple Logistic Regression | 00:00:00 | ||
| 2.4 Goodness of Fit for a Logistic Regression | 00:00:00 | ||
| 2.5 Multicollinearity in Logistic Regression in R | 00:00:00 | ||
| 2.6 Individual Impact of Variables in R | 00:00:00 | ||
| 2.7 Model Selection in R | 00:00:00 | ||
| 2.8 Logistic Regression Conclusion | 00:00:00 | ||
| Logistic Regression Quiz | Unlimited | ||
| Session 3 - Decision Tree | |||
| Handout – Decision Tree in R | 00:00:00 | ||
| 3.1 Introduction to Decision Tree & Segmentation | FREE | 00:00:00 | |
| 3.2 The Decision Tree Philosophy & The Decision Tree Approach | FREE | 00:00:00 | |
| 3.3 The Splitting criterion &Entropy Calculation | 00:00:00 | ||
| 3.4 Information Gain & Calculation | 00:00:00 | ||
| 3.5 The Decision tree Algorithm | 00:00:00 | ||
| 3.6 Split for Variable & The Decision tree-lab(Part 1) | 00:00:00 | ||
| 3.7 The Decision tree-lab(Part 2) & Validation | 00:00:00 | ||
| 3.8 The Decision tree -lab (Part3) & Overfitting | 00:00:00 | ||
| 3.9 Pruning & Complexity Parameters | 00:00:00 | ||
| 3.10 Choosing Cp & Cross Validation Error | 00:00:00 | ||
| 3.11 Two types of Pruning | 00:00:00 | ||
| 3.12 Tree Building & Model Selection-Lab | 00:00:00 | ||
| 3.13 Conclusion | 00:00:00 | ||
| Decision Trees Quiz | Unlimited | ||
| Session 4 - Model Selection and Cross Validation | |||
| Model Selection and Cross Validation in R | 00:00:00 | ||
| 4.1 Introduction to Model Selection | FREE | 00:00:00 | |
| 4.2 Sensitivity Specificity | FREE | 00:00:00 | |
| 4.3 Sensitivity Specificity Continued | 00:00:00 | ||
| 4.4 ROC AUC | 00:00:00 | ||
| 4.5 The Best Model | 00:00:00 | ||
| 4.6 Errors | 00:00:00 | ||
| 4.7 Overfitting Underfitting | 00:00:00 | ||
| 4.8 Bias_Variance Treadoff | 00:00:00 | ||
| 4.9 Holdout Data Validation | 00:00:00 | ||
| 4.10 Ten Fold CV | 00:00:00 | ||
| 4.11 Kfold CV | 00:00:00 | ||
| 4.12 Conclusion | 00:00:00 | ||
| Session 5 - Neural Network | |||
| Handout – Neural Networks in R | 00:00:00 | ||
| 5.1 Introduction and LogReg Recap | FREE | 00:00:00 | |
| 5.2 Decision Boundary | FREE | 00:00:00 | |
| 5.3 Non Linear Decision Boundary NN | 00:00:00 | ||
| 5.4 Non Linear Decision Boundary and Solution | 00:00:00 | ||
| 5.5 Neural Network Intution | 00:00:00 | ||
| 5.6 Neural Networks Algorithm | 00:00:00 | ||
| 5.7 Neural Network Algorithm Demo | 00:00:00 | ||
| 5.8 Building a Neural Network | 00:00:00 | ||
| 5.9 Local vs Global Min | 00:00:00 | ||
| 5.10 Lab Digit Recognizer | 00:00:00 | ||
| 5.10.1 Digit Recognizer Second Attempt Part 1 | 00:00:00 | ||
| 5.10.2 Digit Recognizer Second Attempt Part 2 | 00:00:00 | ||
| 5.11 Conclusion | 00:00:00 | ||
| Neural Networks Quiz | Unlimited | ||
| Session 6 - Support Vector Machine - SVM | |||
| Handout – Support Vector Machine | 00:00:00 | ||
| 6.1 Introduction To SVM | FREE | 00:00:00 | |
| 6.2 The Classifier and Decision Boundary | FREE | 00:00:00 | |
| 6.3 SVM – The Large Margin Classifier | 00:00:00 | ||
| 6.4 The SVM Algorithms and Results | 00:00:00 | ||
| 6.5 SVM on R | 00:00:00 | ||
| 6.6 Non Linear Boundary | 00:00:00 | ||
| 6.7 Kernal Trick | 00:00:00 | ||
| 6.8 Kernal Trick on R | 00:00:00 | ||
| 6.9 Soft Margin and Validataion | 00:00:00 | ||
| 6.10 SVM Advantages, Disadvantages and Applications | 00:00:00 | ||
| 6.11 Lab Digit recognize | 00:00:00 | ||
| 6.12 SVM Conclusion | 00:00:00 | ||
| SVM Quiz | Unlimited | ||
| Session 7 - Random Forest and Boosting | |||
| Random Forest and Boosting in R | 00:00:00 | ||
| 7.1 Introduction to Bagging RF Boosting | FREE | 00:00:00 | |
| 7.2 Wisdom of Crowd | FREE | 00:00:00 | |
| 7.3 Ensemble Learning | 00:00:00 | ||
| 7.4 Ensemble Models | 00:00:00 | ||
| 7.5 Bagging | 00:00:00 | ||
| 7.6 Bagging Models LAB | 00:00:00 | ||
| 7.7 Random Forest | 00:00:00 | ||
| 7.8 Random Forest LAB | 00:00:00 | ||
| 7.9 Boosting | 00:00:00 | ||
| 7.10 Boosting Illustration | 00:00:00 | ||
| 7.11 Boosting LAB | 00:00:00 | ||
| 7.12 Conclusion | 00:00:00 | ||
| RF and Boosting Quiz | Unlimited | ||
| Session 8 - Cluster Analysis | |||
| Handout – Cluster Analysis | 00:00:00 | ||
| 8.1 Introduction to Clustering via Segmentation | FREE | 00:00:00 | |
| 8.2 Types of Clusters | FREE | 00:00:00 | |
| 8.3 Similarities and Dissimilarity | 00:00:00 | ||
| 8.4 Calculating the Distance | 00:00:00 | ||
| 8.5 Calculating Distance in R | 00:00:00 | ||
| 8.6 Clustering Algorithms – Kmeans | 00:00:00 | ||
| 8.7 Kmeans Clustring on R | 00:00:00 | ||
| 8.8 More on Kmeans | 00:00:00 | ||
| 8.9 Data Stanndardisation and Non-numeric Data | 00:00:00 | ||
| 8.10 Conclusion | 00:00:00 | ||
| Cluster Analysis | Unlimited | ||
| Part 3 – Machine Learning Projects using R | |||
| Consumer Loan Default Prediction | 00:00:00 | ||
| Bank Tele Marketing | 00:00:00 | ||
| Automobile Pricing Strategy | 00:00:00 | ||
| Census Income | 00:00:00 | ||
| Direct Mail Marketing | 00:00:00 | ||
| Credit Card Ratings | 00:00:00 | ||
477 STUDENTS ENROLLED
