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### Course Curriculum

 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 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 00:00:00 2.6 Individual Impact of Variables 00:00:00 2.7 Model Selection 00:00:00 2.8 Logistic Regression Conclusion 00:00:00 Logistic Regression Quiz Unlimited Session 3 - Decision Tree Handout – Decision Tree 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 Handout – Model Selection and Cross Validation 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 Model selection and cross validation Quiz 00:10:00 Session 5 - Neural Network Handout – Neural Networks 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 Handout – Random Forest and Boosting 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
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