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Four Rare Machine Learning Skills All Data Scientists Need
  • Level Expert
  • المدة 6 ساعات hours
  • الطبع بواسطة SAS
  • Offered by

عن

This course covers the most neglected yet critical skills in machine learning, four vital techniques that are very rarely covered – most courses and books omit them entirely. 1) UPLIFT MODELING (AKA PERSUASION MODELING): When you're modeling, are you even predicting the right thing? 2) THE ACCURACY FALLACY: When evaluating how well a model works, are you even reporting on the right thing? 3) P-HACKING: Are your simplest discoveries from data even real? 4) THE PARADOX OF ENSEMBLE MODELS: Do you understand how they work, even though they seem to defy Occam's Razor? >> WHY THESE ADVANCED METHODS ARE ESSENTIAL: Each one addresses a question that is fundamental to machine learning (above). For many projects, success hinges on these particular skills. >> NO HANDS-ON – BUT FOR TECHNICAL LEARNERS: This course has no coding and no use of machine learning software. Instead, it lays the conceptual groundwork before you take on the hands-on practice. When it comes to these state-of-the-art techniques and prevalent pitfalls, there's a foundation of conceptual knowledge to build before going hands-on – and you'll be glad you did. >> VENDOR-NEUTRAL: This course includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.

الوحدات

Course introduction

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Course overview

1 Readings

  • The Machine Learning Glossary (optional)

Uplift modeling

    • 4 Videos
    • 1 Readings
    • 2 Discussion
    • 4 Assignment
Show info about module content

4 Assignment

  • Uplift modeling I: optimize for influence and persuade by the numbers
  • Uplift modeling II: modeling over treatment and control groups
  • Uplift modeling III: how it works – for banks and for Obama
  • Uplift modeling IV: improving churn modeling, plus other applications

2 Discussions

  • Your biggest surprise and most important learning from this lesson
  • Your most pressing unanswered question

4 Videos

  • Uplift modeling I: optimize for influence and persuade by the numbers
  • Uplift modeling II: modeling over treatment and control groups
  • Uplift modeling III: how it works – for banks and for Obama
  • Uplift modeling IV: improving churn modeling, plus other applications

1 Readings

  • Complementary readings on uplift modeling (optional)

The accuracy fallacy

    • 2 Videos
    • 1 Readings
    • 2 Discussion
    • 2 Assignment
Show info about module content

2 Assignment

  • Accuracy fallacy: orchestrating the media's bogus coverage of ML
  • More accuracy fallacies: predicting psychosis, criminality, & bestsellers

2 Discussions

  • Your biggest surprise and most important learning from this lesson
  • Your most pressing unanswered question

2 Videos

  • Accuracy fallacy: orchestrating the media's bogus coverage of ML
  • More accuracy fallacies: predicting psychosis, criminality, & bestsellers

1 Readings

  • Complementary reading related to the accuracy fallacy (optional)

P-hacking

    • 3 Videos
    • 1 Readings
    • 2 Discussion
    • 3 Assignment
Show info about module content

3 Assignment

  • P-hacking: a treacherous pitfall
  • P-hacking: your predictive insights may be bogus
  • P-hacking: how to ensure sound discoveries

2 Discussions

  • Your biggest surprise and most important learning from this lesson
  • Your most pressing unanswered question

3 Videos

  • P-hacking: a treacherous pitfall
  • P-hacking: your predictive insights may be bogus
  • P-hacking: how to ensure sound discoveries

1 Readings

  • Complementary materials on p-hacking (optional)

The paradox of ensemble models

    • 3 Videos
    • 1 Readings
    • 2 Discussion
    • 2 Assignment
Show info about module content

2 Assignment

  • Ensemble models and the Netflix Prize
  • Supercharging prediction: ensembles & the generalization paradox

2 Discussions

  • Your biggest surprise and most important learning from this lesson
  • Your most pressing unanswered question

3 Videos

  • Ensemble models and the Netflix Prize
  • Supercharging prediction: ensembles & the generalization paradox
  • DEMO - Training an ensemble model (optional)

1 Readings

  • The generalization paradox of ensembles (optional)

Course review

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded course completion quiz

1 Videos

  • Course conclusions

1 Readings

  • Further learning options

Auto Summary

Discover the often-overlooked yet crucial skills every data scientist needs with the "Four Rare Machine Learning Skills All Data Scientists Need" course. Dive into the fundamental techniques that are rarely covered in conventional learning paths: 1. **Uplift Modeling (Persuasion Modeling)**: Learn if you're predicting the right outcomes in your models. 2. **The Accuracy Fallacy**: Understand the proper metrics to evaluate your model's performance. 3. **P-Hacking**: Discern the reality of your data discoveries and avoid common pitfalls. 4. **The Paradox of Ensemble Models**: Grasp the workings of complex models that challenge simplicity principles. This expert-level course is designed for technical learners who want to strengthen their conceptual foundation before applying hands-on skills. It provides a comprehensive understanding of these advanced methodologies without the need for coding or specific machine learning software. Although software demos using SAS products are included, the principles taught are universally applicable across different tools. Guided by Coursera, this 360-minute course is available through a Starter subscription and is tailored for data scientists eager to master the nuanced aspects of machine learning. Prepare to enhance your expertise and ensure success in your machine learning projects by addressing these essential, yet frequently neglected, skills.

Eric Siegel
Instructor

Eric Siegel