Coursera - Machine Learning Rock Star – the End-to-End Practice Specialization by SAS

An End-to-End Guide to Leading and Launching ML. This expansive machine learning curriculum is accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.

The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections.

Want to tap that potential? It's best to start with a holistic, business-oriented course on machine learning – no matter whether you’re more on the tech or the business side. After all, successfully deploying machine learning relies on savvy business leadership just as much as it relies on technical skill. And for that reason, data scientists aren't the only ones who need to learn the fundamentals. Executives, decision makers, and line of business managers must also ramp up on how machine learning works and how it delivers business value.

And the reverse is true as well: Techies need to look beyond the number crunching itself and become deeply familiar with the business demands of machine learning. This way, both sides speak the same language and can collaborate effectively.

This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning. It covers:

– How launching machine learning – aka predictive analytics – improves marketing, financial services, fraud detection, and many other business operations

– A concrete yet accessible guide to predictive modeling methods, delving most deeply into decision trees

– Reporting on the predictive performance of machine learning and the profit it generates

– What your data needs to look like before applying machine learning

– Avoiding the hype and false promises of “artificial intelligence”

– AI ethics: social justice concerns, such as when predictive models blatantly discriminate by protected class

Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate.

But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice. This means that two different species must cooperate in harmony: the business leader and the quant.

This course will guide you to lead or participate in the end-to-end implementation of machine learning (aka predictive analytics). Unlike most machine learning courses, it prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment.

Whether you'll participate on the business or tech side of a machine learning project, this course delivers essential, pertinent know-how. You'll learn the business-level fundamentals needed to ensure the core technology works within - and successfully produces value for - business operations. If you're more a quant than a business leader, you'll find this is a rare opportunity to ramp up on the business side, since technical ML trainings don't usually go there. But know this: The soft skills are often the hard ones.

After this course, you will be able to:
- Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more.
- Plan ML: Determine the way in which machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there.
- Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues.
- Lead ML: Manage a machine learning project, from the generation of predictive models to their launch.
- Prep data for ML: Oversee the data preparation, which is directly informed by business priorities.
- Evaluate ML: Report on the performance of predictive models in business terms, such as profit and ROI.
- Regulate ML: Manage ethical pitfalls, such as when predictive models reveal sensitive information about individuals, including whether they're pregnant, will quit their job, or may be arrested - aka AI ethics.

Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
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