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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