This course will help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.

Here is how the course is going to work:

- Part 1 – Introduction to R Programming.
- This is the part where you will learn basic of R programming and familiarize yourself with R environment.
- Be able to import, export, explore, clean and prepare the data for advance modeling.
- Understand the underlying statistics of data and how to report/document the insights.

- Part 2 – Machine Learning using R
- Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
- Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it.
- Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

- Part 3 – Machine Learning Projects using R
- Apply all your learning into projects on problems from various domains.
- Full comprehensive Data Science pipeline from raw data to model building and making final predictions.

### 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 |

**476 STUDENTS ENROLLED**