This course covers the most neglected yet critical skills in machine learning, four vital techniques that are very rarely covered 鈥� most courses and books omit them entirely. 1) UPLIFT MODELING (AKA PERSUASION MODELING): When you're modeling, are you even predicting the right thing? 2) THE ACCURACY FALLACY: When evaluating how well a model works, are you even reporting on the right thing? 3) P-HACKING: Are your simplest discoveries from data even real? 4) THE PARADOX OF ENSEMBLE MODELS: Do you understand how they work, even though they seem to defy Occam's Razor? >> WHY THESE ADVANCED METHODS ARE ESSENTIAL: Each one addresses a question that is fundamental to machine learning (above). For many projects, success hinges on these particular skills. >> NO HANDS-ON 鈥� BUT FOR TECHNICAL LEARNERS: This course has no coding and no use of machine learning software. Instead, it lays the conceptual groundwork before you take on the hands-on practice. When it comes to these state-of-the-art techniques and prevalent pitfalls, there's a foundation of conceptual knowledge to build before going hands-on 鈥� and you'll be glad you did. >> 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.
Course overview
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Skills You'll Learn
Data Science, Marketing Analytics, Predictive Modeling, Predictive Analytics, Statistical Machine Learning, Applied Machine Learning, Technical Communication, Customer Retention, Statistical Analysis, Machine Learning
Taught By
Eric Siegel
Founder of Machine Learning Week and GenAI World, executive editor of "The Machine Learning Times", author of "The AI Playbook" and "Predictive Analytics"