Machine learning. It is essential for your team and your boss, and a career-enhancing skill. LinkedIn ranks it as one of the "Skills Companies Most Need" in the United States and the most sought-after emerging job.
To participate in the deployment of machine learning and predictive analysis, you must have a good understanding of these concepts. You must be able understand machine learning and predictive analytics, even if you are a business leader. You need to understand how predictive models are used to make decisions, regardless of whether you're an executive, decision maker, or operational manager.
It is worthwhile to look under the hood. Machine learning is an intriguing science that can be intuitively interpreted. Machine learning is having a rapid growing impact on the world. It is crucial to understand its predictive power and scientific implications.
This course will cover machine learning. This course will cover the basics of machine learning and how insights can be obtained from data. This course also covers how to ensure that these insights are reliable. It also demonstrates the effectiveness of predictive models. This can all be done using very basic math. These are vital information every business professional must know.
This course covers more than just machine learning techniques. It also covers advanced, cutting-edge techniques and how to avoid common pitfalls. These topics are well-covered, but the course can be used by both technical and non-technical learners.
This course will show you the difference between what works and what doesn't.
- Predictive modeling algorithms work, including logistic regressions and neural networks.
There are many dangers, such as hacking, overfitting and presuming that all correlations are causal.
How to interpret and explain how a predictive model works
Advanced methods such as ensembles and persuasion modeling (also called persuasion modeling) are possible.
How do you choose the right tool among all available machine learning software options?
How to evaluate a predictive model in order to report its performance in business terms
How to screen a predictive model for biases against protected groups Also called AI ethics.
IN-DEPTH YET ACCESSIBLE. This curriculum was created by Eric Siegel, an industry leader and winner of Columbia University's teaching awards. This course is unique and engaging. It's also very accessible.
NO HANDS-ON, HEAVY MATH. This course does not provide hands-on training, but rather an overview of the most recent techniques and dangerous pitfalls. This course is useful for both data scientists and business leaders. These exercises don't require any coding or machine-learning software. However, for one assessment you will need to create a predictive model using Excel or Google Sheets and then see how it changes before your eyes.
BUT, TECHNICAL LEARNERS MUST TAKE A OTHER LOOK. This curriculum provides complementary knowledge that top techies should also learn. This curriculum provides a solid conceptual framework that contextualizes core technology. This curriculum covers topics that are often left out of technical courses like persuasion and uplift modeling.
VENDOR-NEUTRAL. The course includes software demonstrations that show machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives will apply regardless of which machine learning software you use.
PREREQUISITES. Before taking this course, learners must have taken the first two courses in this specialization: "The Power of Machine Learning" and "Launching Machine Learning".
