Machine learning is a field which involves in transforming raw data into data of actionable knowledge. The prominence of R language is increasing day by day as it is a cross platform and a zero cost statistical tool. Machine learning with R offers a powerful set of methods for quick and easy access of data.
- Contents
- Correlation
- Simple Regression
- Logistic regression
- What is segmentation
- What is a Decision tree
- Tree validation
- Over fitting and Under fitting
- Cross validation
- Boot strapping
- Neural networks
- SVM
- Random Forest algorithm
- Boosting
- After the Completion of This Course, one will have a good hands on machine learning with R programming. R will make the various machine learning technique more simpler.
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 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 | |||
Handout – 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 | |||
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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