Uplift Modeling Part 2: Modeling Strategies

1. 01S Learner Model7 lessons · 32 min

   1. Introduction to Uplift ModelingPreview6:23
   2. Download Resources1:00
   3. Building the S-Learner Model from scratch10:46
   4. How to Make Predictions?1:29
   5. Compute Uplift - ITE and ATE4:40
   6. Customer Segmentation3:23
   7. Assignment: QINI Curve4:33
2. 02Modeling Multiple Treatments4 lessons · 14 min

   1. Modeling Approach2:41
   2. Training the model6:18
   3. Course Review1:00
   4. Make predictions and interpret3:42
3. 03T Learner Model3 lessons · 7 min

   1. How T Learner works?2:59
   2. Building the T Learner model1:58
   3. Observations1:51
4. 04X Learner Model5 lessons · 30 min

   1. How the X Learner Works?18:11
   2. Build Stage 1 model: Treatment and control2:35
   3. Build Stage 2 model: Predict imputed uplift1:21
   4. Build Stage 3 model: Predict Propensity score5:44
   5. More Resources for advanced modeling2:10

What you need

• Basics of Python
• Foundational knowledge of Data Science
• 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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