SAS Institute Of Management Studies - Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
- Offered byCoursera
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls at Coursera Overview
Duration | 17 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 3 in the Machine Learning Rock Star ' the End-to-End Practice
Specialization - Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML ' whether you're an enterprise leader or a quant.
- Approx. 17 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls at Coursera Course details
- 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."
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls at Coursera Curriculum
MODULE 1 - The Foundational Underpinnings of Machine Learning
Course overview: Machine Learning Under the Hood
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
Why ice cream is linked to shark attacks
Causation is just a hobby -- prediction is your job
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)
Why this course isn't hands-on & why it's essential for techies anyway
The Machine Learning Glossary
One-question survey
Complementary materials on p-hacking (optional)
Correlation does not imply causation (optional)
Data access for auditors (optional)
Course overview: Machine Learning Under the Hood
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
Why ice cream is linked to shark attacks
Causation is just a hobby -- prediction is your job
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)
Module 1 Review
MODULE 2 - Standard, Go-To Machine Learning Methods
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
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)
A powerful, helpful visualization of how decision trees work (optional)
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
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
Module 2 Review
MODULE 3 - Advanced Methods, Comparing Methods, & Modeling Software
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)
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 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
The generalization paradox of ensembles (optional)
Complementary readings on uplift modeling (optional)
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
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 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
Module 3 Review
MODULE 4 ? Pitfalls, Bias, and Conclusions
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
Pitfalls: the seven deadly sins of machine learning
Conclusions and what's next ? continuing your learning
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)
John Elder's top ten data science mistakes (optional)
Further resources and readings to continue your learning (optional)
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
Pitfalls: the seven deadly sins of machine learning
Conclusions and what's next - continuing your learning
Module 4 Review
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls at Coursera Admission Process
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