0
  • LOGIN

  • No products in the cart.

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
Model selection and cross validation Quiz 00:10:00
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

Related Courses

TAKE THIS COURSE
  • ₹5,999
  • UNLIMITED ACCESS
471 STUDENTS ENROLLED
  • Profile photo of Venkata Reddy
  • Profile photo of Debendra Das
  • Profile photo of NAVEEN GUPTA JALLURI
  • Profile photo of Sameer Patil