In this article, I identify extraordinary unmet learner needs and address them with a free offering: my business-oriented machine learning course series, which is designed to fulfill those needs – three vendor-neutral courses that deliver material critical for both techies and business leaders. If you or members of your team would benefit from taking the course series, see how to access it for free here.
Over the last few years, I poured thousands of working hours and 25 years of consulting and teaching experience into making a course series called “Machine Learning for Everyone” (now live and free to access on Coursera).
Why? I developed this training program because teaching is in my blood and I was dying to fulfill two unmet training requirements:
A comprehensive, accessible go-to for business professionals that empowers them to generate business value with machine learning
A must-take for everyone involved with machine learning (both techies and business leaders) that uniquely supplements technical machine learning courses by covering critical material that’s normally skipped over
What critical material do ML courses normally skip over? Two main things. The business side best practices – including a very particular management process. And a business-oriented, lay-friendly dive into the core tech that’s understandable even to learners without a technical background – including how it works, how well it needs to work, and why it often doesn't work.
Now, one thing's for sure: ML is booming. It reinvents industries and runs the world. According to the Harvard Business Review, ML is “the most important general-purpose technology of our era.”
But ML presents a great management challenge: It requires an in-depth business-side understanding of the technology and a very particular business leadership process. That is to say that, to use ML, you need both business leadership and data science – and both sides need to learn both sides in order to successfully collaborate, jointly plan, and jointly execute.
The most common mistake that derails ML projects is to jump into the ML itself, the actual number crunching, before establishing a path to operational deployment. ML isn't a technology you simply buy and plug in. Rather, you’re embarking upon a new kind of value proposition, and so it requires a new kind of leadership process.
The use of ML far transcends its core number crunching. Think of ML not as a technology but as an organizational paradigm that leads to improved operations. To follow this paradigm, you've got to bridge what is a prevalent gap between business leadership and technical know-how. You must bridge this quant/business cultural divide by way of a wholly collaborative process guided jointly by strategic, operational, and analytical stakeholders.
That is to say that, for ML to deliver value, two different “species” must cooperate in harmony: the business leader and the quant. In order to function together, they each have to adapt. On the one hand, the quant needs to attain a business-oriented vantage. And on the other, the business leader must navigate a very alien world.
Bridging this gargantuan divide is worth the effort. If you construct a durable bridge across that gap, you can achieve the value of ML deployment. Applying the core algorithms – which learn from data to predict – is only half of the trick. Beyond the technical process, there’s an organizational process. Since existing business operations must change by way of implementing analytics, it’s no longer business as usual. Science now drives your enterprise’s greatest pipelines of decisions and actions. In this way, deploying ML is intrinsically revolutionary.
So you must precisely plan for how it's going to be deployed. For each initiative, you’ve gotta clear a path – from the get-go – that will lead to machine learning’s integration. This requires a socialization of buy-in: Line of business leaders and managers must agree to make a real change to operations. To that end, they must learn what the predictions generated by ML do for them and they must be willing to put their faith in them.
Now, the entire ML industry is compromised because, the thing is, this often doesn’t work out. As they say... well, as Hulya Farinas put it, “At companies where there is no framework for the operationalization of models, PowerPoint is where [predictive] models go to die!”
But organizations that follow best practices in ML leadership thrive.
So, whether you'll participate on the business or tech side of a ML project, the business-side fundamentals of ML make for essential, pertinent know-how. They're needed in order to ensure the core technology works within – and successfully produces value for – business operations.
Those who are more a quant than a business leader will find this curriculum to be a rare opportunity to ramp up on the business side, since technical ML trainings don’t usually go there. Data wonks must know this: The soft skills are often the hard ones.
To dive in, I invite you – and your team – to take my three-course series (accessible at no cost).
Click here for a one-page breakdown (PDF) of what material is uniquely covered by the course series.
See also: Seven Reasons Budding Data Scientists Need a Machine Learning Course That’s Not Hands-On and Coursera’s “Machine Learning for Everyone” Fulfills Unmet Training Requirements
Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of the long-running Predictive Analytics World and the Deep Learning World conference series, which have served more than 17,000 attendees since 2009, the instructor of the end-to-end, business-oriented Coursera specialization Machine learning for Everyone, a popular speaker who's been commissioned for more than 100 keynote addresses, and executive editor of The Machine Learning Times. He authored the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at more than 35 universities, and he won teaching awards when he was a professor at Columbia University, where he sang educational songs to his students. Eric also publishes op-eds on analytics and social justice.