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Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
  • Level Foundation
  • Duration 17 hours
  • Course by SAS
  • Offered by

About

Machine learning. Your team needs it, your boss demands it, and your career loves it. After all, LinkedIn places it as one of the top few "Skills Companies Need Most" and as the very top emerging job in the U.S. If you want to participate in the deployment of machine learning (aka predictive analytics), you've got to learn how it works. Even if you work as a business leader rather than a hands-on practitioner – even if you won't crunch the numbers yourself – you need to grasp the underlying mechanics in order to help navigate the overall project. Whether you're an executive, decision maker, or operational manager overseeing how predictive models integrate to drive decisions, the more you know, the better. And yet, looking under the hood will delight you. The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by. With its impact on the world growing so quickly, it's time to demystify the predictive power of data – and how to scientifically tap it. This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants. And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers. With this course, you'll learn what works and what doesn't – the good, the bad, and the fuzzy: – How predictive modeling algorithms work, including decision trees, logistic regression, and neural networks – Treacherous pitfalls such as overfitting, p-hacking, and presuming causation from correlations – How to interpret a predictive model in detail and explain how it works – Advanced methods such as ensembles and uplift modeling (aka persuasion modeling) – How to pick a tool, selecting from the many machine learning software options – How to evaluate a predictive model, reporting on its performance in business terms – How to screen a predictive model for potential bias against protected classes – aka AI ethics IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning. NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike with expansive coverage of the state-of-the-art techniques and the most pernicious pitfalls. There are no exercises involving coding or the use of machine learning software. However, for one of the assessments, you'll perform a hands-on exercise, creating a predictive model by hand in Excel or Google Sheets and visualizing how it improves before your eyes. BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology with a strong conceptual framework and covers topics that are generally omitted from even the most technical of courses, including uplift modeling (aka persuasion modeling) and some particularly treacherous pitfalls. 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. PREREQUISITES. Before this course, learners should take the first two of this specialization's three courses, "The Power of Machine Learning" and "Launching Machine Learning."

Modules

Course Introduction

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

1 Assignment

  • Course overview: Machine Learning Under the Hood

1 Videos

  • Course overview: Machine Learning Under the Hood

3 Readings

  • Why this course isn't hands-on & why it's essential for techies anyway
  • The Machine Learning Glossary
  • One-question survey

Ensuring Discoveries Are Trustworthy

    • 4 Videos
    • 1 Readings
    • 4 Assignment
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4 Assignment

  • P-hacking: a treacherous pitfall
  • P-hacking: your predictive insights may be bogus
  • P-hacking: how to ensure sound discoveries
  • Avoiding overfitting: the train/test split

4 Videos

  • P-hacking: a treacherous pitfall
  • P-hacking: your predictive insights may be bogus
  • P-hacking: how to ensure sound discoveries
  • Avoiding overfitting: the train/test split

1 Readings

  • Complementary materials on p-hacking (optional)

Correlation Does Not Imply Causation

    • 2 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Why ice cream is linked to shark attacks
  • Causation is just a hobby -- prediction is your job

2 Videos

  • Why ice cream is linked to shark attacks
  • Causation is just a hobby -- prediction is your job

1 Readings

  • Correlation does not imply causation (optional)

The Principles of Predictive Modeling

    • 3 Videos
    • 1 Readings
    • 1 PeerReview
    • 3 Assignment
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3 Assignment

  • The art of induction: why generalizing from data is hard
  • Learning from mistakes: why negative cases matter
  • Intro to the hands-on assessment (Excel or Google Sheets)

1 Peer Review

  • Form a predictive model by hand to increase lift

3 Videos

  • The art of induction: why generalizing from data is hard
  • Learning from mistakes: why negative cases matter
  • Intro to the hands-on assessment (Excel or Google Sheets)

1 Readings

  • Data access for auditors (optional)

Review

    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 1 Review

2 Discussions

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

Decision Trees: a Great Place to Start

    • 6 Videos
    • 1 Readings
    • 1 ExternalTool
    • 5 Assignment
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5 Assignment

  • A refresher on decision trees
  • Business rules rock and decision trees rule
  • Pruning decision trees to avoid overfitting
  • Drawing the gains curve for a decision tree
  • Drawing the profit curve for a decision tree

1 External Tool

  • Access SAS Viya for Learners (for optional hands-on demo)

6 Videos

  • A refresher on decision trees
  • Business rules rock and decision trees rule
  • Pruning decision trees to avoid overfitting
  • DEMO - Comparing decision tree models (optional)
  • Drawing the gains curve for a decision tree
  • Drawing the profit curve for a decision tree

1 Readings

  • A powerful, helpful visualization of how decision trees work (optional)

Beyond Trees: Other Standard Modeling Methods

    • 6 Videos
    • 1 ExternalTool
    • 5 Assignment
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5 Assignment

  • Naïve Bayes
  • Linear models and perceptrons
  • Linear part II: a perceptron in two dimensions
  • Why probabilities drive better decisions than yes/no outputs
  • Logistic regression

1 External Tool

  • Access SAS Viya for Learners (for optional hands-on demo)

6 Videos

  • Naïve Bayes
  • Linear models and perceptrons
  • Linear part II: a perceptron in two dimensions
  • Why probabilities drive better decisions than yes/no outputs
  • Logistic regression
  • DEMO - Training a logistic regression model (optional)

Review

    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 2 Review

2 Discussions

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

Advanced Modeling Methods

    • 8 Videos
    • 1 Readings
    • 2 ExternalTool
    • 5 Assignment
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5 Assignment

  • How neural networks work
  • Neural nets: decision boundaries & a comparison to logistic regression
  • Deep learning
  • Ensemble models and the Netflix Prize
  • Supercharging prediction: ensembles & the generalization paradox

2 External Tool

  • Access SAS Viya for Learners (for optional hands-on demo)
  • Access SAS Viya for Learners (for optional hands-on demo)

8 Videos

  • How neural networks work
  • Neural nets: decision boundaries & a comparison to logistic regression
  • DEMO - Training a neural network model (optional)
  • Deep learning
  • Ensemble models and the Netflix Prize
  • Supercharging prediction: ensembles & the generalization paradox
  • DEMO - Training an ensemble model (optional)
  • DEMO - Autotuning a machine learning model (optional)

1 Readings

  • The generalization paradox of ensembles (optional)

Modeling Methods Overview: Summary, Software, and Deployment

    • 4 Videos
    • 4 Assignment
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4 Assignment

  • Compare and contrast: summary of ML methods
  • Machine learning software: dos and don'ts for choosing a tool
  • Machine learning software: how tools vary and how to choose one
  • Model deployment: out of the software tool and into the field

4 Videos

  • Compare and contrast: summary of ML methods
  • Machine learning software: dos and don'ts for choosing a tool
  • Machine learning software: how tools vary and how to choose one
  • Model deployment: out of the software tool and into the field

Uplift Modeling (aka Persuasion Modeling)

    • 4 Videos
    • 1 Readings
    • 4 Assignment
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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

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)

Review

    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 3 Review

2 Discussions

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

Ethics: Machine Bias, Model Transparency, and Conclusions

    • 5 Videos
    • 6 Readings
    • 1 Discussion
    • 5 Assignment
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5 Assignment

  • Machine bias I: the conundrum of inequitable models
  • Machine bias II: visualizing why models are inequitable
  • Machine bias III: justice can't be colorblind
  • Explainable ML, model transparency, and the right to explanation
  • Conclusions on ML ethics: establishing standards as a form of social activism

1 Discussions

  • What are your greatest ethical concerns about the application of machine learning?

5 Videos

  • Machine bias I: the conundrum of inequitable models
  • Machine bias II: visualizing why models are inequitable
  • Machine bias III: justice can't be colorblind
  • Explainable ML, model transparency, and the right to explanation
  • Conclusions on ML ethics: establishing standards as a form of social activism

6 Readings

  • The original ProPublica article on machine bias
  • Interactive MIT Technology Review article on disparate false positive rates
  • Another interactive demo of machine bias (optional)
  • Complementary reading on machine bias (optional)
  • More on explainable ML and model transparency (optional)
  • Tallying the positive and negative impacts of AI (optional)

Specialization Wrap-Up

    • 2 Videos
    • 2 Readings
    • 2 Assignment
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2 Assignment

  • Pitfalls: the seven deadly sins of machine learning
  • Conclusions and what's next - continuing your learning

2 Videos

  • Pitfalls: the seven deadly sins of machine learning
  • Conclusions and what's next – continuing your learning

2 Readings

  • John Elder's top ten data science mistakes (optional)
  • Further resources and readings to continue your learning (optional)

Review

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Module 4 Review

1 Discussions

  • What's your next step in machine learning?

Auto Summary

Unlock the world of machine learning with this comprehensive course led by industry expert Eric Siegel. Ideal for business leaders and aspiring data scientists, you'll dive into predictive modeling, advanced methods, and ethical considerations without heavy math or hands-on coding. Spanning 1020 minutes, this vendor-neutral course offers foundational insights and practical strategies to navigate machine learning projects effectively. Available on Coursera with Starter and Professional subscription options, it's perfect for those seeking to enhance their understanding and application of AI in business.

Eric Siegel
Instructor

Eric Siegel