Dismantling the lie of artificial intelligence – from 'Machine Learning Leadership and Practice'

Check out my latest Forbes article (04.10.2024), "Elon Musk Predicts Artificial General Intelligence In 2 Years. Here’s Why That’s Hype":

From the course "The Power of Machine Learning", part of the three-course specialization, "Machine Learning Leadership and Practice – End-to-End Mastery".

This expansive, end-to-end course series will empower you to launch machine learning. 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.

After three courses, you will be able to:

Lead ML: Manage or participate in the end-to-end implementation of machine learning

Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more

Greenlight ML: Forecast the effectiveness of and scope the requirements for a machine learning project and then internally sell it to gain buy-in

Regulate ML: Manage ethical pitfalls, the risks to social justice that stem from machine learning

ABOUT THIS SPECIALIZATION OF THREE COURSES

Machine learning is booming. It reinvents industries and runs the world. According to the Harvard Business Review, machine learning is “the most important general-purpose technology of our era.”

But while there are so many how-to courses for hands-on techies, there are practically none that also serve business leaders – a striking omission, since success with machine learning relies on a very particular business leadership practice just as much as it relies on adept number crunching.

This specialization fills that gap. It empowers you to generate value with machine learning by ramping you up on both the technical side and the business side – both the cutting edge modeling algorithms and the project management skills needed for successful deployment.

NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists alike with expansive, holistic coverage of the state-of-the-art techniques and business-level best practices. There are no exercises involving coding or the use of machine learning software.

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, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact.

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 specialization stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.

WHAT YOU'LL LEARN: How ML works, how to report on its ROI and predictive performance, best practices to lead an ML project, technical tips and tricks, how to avoid the major pitfalls, whether true AI is coming or is just a myth, the risks to social justice that stem from ML.

DYNAMIC CONTENT. Across this range of topics, this specialization keeps things action-packed with case study examples, software demos, stories of poignant mistakes, and stimulating assessments.

VENDOR-NEUTRAL. This specialization includes several illuminating software demos of machine learning in action using SAS products, plus one hands-on exercise using Excel or Google Sheets. 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.

WHO IT’S FOR. This concentrated entry-level program is totally accessible to business-level learners – and yet also vital to data scientists who want to secure their business relevance. It’s for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you’ll do so in the role of enterprise leader or quant.

[marigeo]

Where is “AI is a big fat lie”? Did you remove it?

[MrDarwin-pncr]

I LOVE your videos! I have been saying the exact same thing everytime I run into someone claiming AI will destroy us all....I would also like to address the concept of what I like to call the "digital barrier". I approach the fallacy of AI on a physical level; on what a computer actually is ( I am an electrical engineer by occupation). A computer only "sees" 0s and 1s ( 5 volts, logic high. 0 volts logic low). A set of these voltage states, say: etc. might represent the word "cat". BUT: 1. ONLY TO US. 2. ONLY after translated into an ANALOG representation that we can perceive. Under the same scenario of a picture of a cat, a computer only "sees" 0s and 1s from its input from a camera. There is a barrier that separates the digital stream from becoming something analog; a computer has no concept of anything other than switching MOSFETS turning on and off. Even a child can simply glance at a picture of a cat and know instantly what it is, what it would be like to pet it, how soft its fur is etc. A computer is INCAPABLE of correlating any of these things together, and cannot actually "see" anything in the real world!

[EricSiegelPredicts]

Check out my latest Forbes article (04.10.2024), "Elon Musk Predicts Artificial General Intelligence In 2 Years. Here’s Why That’s Hype":