Regression modeling in R

What you'll be able to do

• Understand the maths behind regression algorithms
• Build regression models from scratch
• Diagnose and interpret models
• Graphical understanding
• Solve industrial projects (education and banking)
• Domain based data science strategies
• Derive high business impact insights

1. 01Overview3 lessons · 3 min

   1. Course OverviewPreview1:22
   2. How to Get Queries Resolved1:00
   3. Download Resources1:00
2. 02Introduction to Linear Regression5 lessons · 11 min

   1. What is Linear RegressionPreview4:42
   2. Understanding with Practical ExamplesPreview1:56
   3. Types of Linear RegressionPreview1:03
   4. Examples of Industrial Applications1:45
   5. Statistical Modeling vs Machine Learning1:38
3. 03Graphical Understanding3 lessons · 8 min

   1. Graphical Understanding3:15
   2. Formulate Line of Best Fit3:41
   3. Course Review1:00
4. 04Building and Interpreting Linear Regression4 lessons · 24 min

   1. Linear Regression from Scratch using Formula7:14
   2. R-Squared Explained7:04
   3. Pre-Model Build Analysis5:12
   4. Building and Interpreting Linear Regression Models4:52
5. 05Measures of Goodness of Fit6 lessons · 30 min

   1. Problem with R-Squared2:14
   2. Adjusted R-Squared1:26
   3. F-Statistic, AIC, BIC2:54
   4. R-Demo4:00
   5. Assumptions18:15
   6. Course Review1:00
6. 06Project - Linear Regression19 lessons · 1 h 52 min

   1. Problem Statement2:37
   2. Handling Missing Values9:45
   3. Outlier Analysis4:09
   4. Graphical and Statistical Analysis9:57
   5. Building Linear Regression6:46
   6. Good Model2:30
   7. Evaluation Measures5:25
   8. Tips to Improve Model Accuracy4:18
   9. Need for Cross Validation11:31
   10. Cross Validation Approaches4:48
   11. Variable Transformations and Interactions5:20
   12. Variance Inflation Factor3:31
   13. Cooks Distance for Influential Points6:13
   14. Cooks Distance Demo4:35
   15. BoxCox and YeoJohnson Transformations6:10
   16. Residual Analysis5:32
   17. Overcoming Heteroscedasticity8:04
   18. Stepwise Regression for Model Search5:25
   19. Best Subsets Model Search5:42
7. 07Gradient Descent6 lessons · 23 min

   1. What Exactly is Gradient Descent7:42
   2. How Gradient Descent Learns6:28
   3. Comparing Types of Gradient Descent2:42
   4. Stopping Criteria and Scaling1:50
   5. Types of Gradient Descent3:32
   6. Course Review1:00
8. 08Introduction to Logistic Regression3 lessons · 9 min

   1. Introduction to Logistic Regression2:39
   2. One vs Rest Strategy2:01
   3. Use Case Examples4:18
9. 09Understanding the Math4 lessons · 11 min

   1. Math behind Logistic Regression Part 13:38
   2. Math behind Logistic Regression Part 23:54
   3. Why Negative Logloss2:46
   4. Course Review1:00
10. 10Project - Logistic Regression14 lessons · 1 h 13 min

    1. Problem Statement Marketing1:16
    2. Demo - EDA for Logit8:34
    3. Building Logistic Regression Model4:43
    4. McFadden's R-Squared2:49
    5. Confusion Matrix and Evaluation Metrics8:45
    6. Precision Recall Curve3:25
    7. Optimal Cutoff Score4:37
    8. The ROC Curve1:32
    9. KS Statistic and Gain Curve11:22
    10. Concordant and Discordant pairs5:33
    11. Approaches to Handle Class Imbalance5:52
    12. Cost Sensitive Learning6:24
    13. Oversampling2:48
    14. Hybrid Sampling5:20

What you need

• Basics of R Programming
• High School Maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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