Machine Learning Rock Star – the End-to-End Practice is a comprehensive course offered by SAS, designed to empower business leaders and data scientists with the expertise to generate value through machine learning. This end-to-end guide covers both the core technology and the business-side practice, providing a holistic understanding of machine learning.
The course is tailored for individuals leading or participating in the deployment of machine learning, offering expansive coverage without hands-on technical training. Participants will gain insights into how machine learning works, reporting on its ROI and predictive performance, best practices for leading ML projects, technical tips and tricks, and ethical considerations.
This course is a valuable resource for business leaders and burgeoning data scientists seeking to understand the practical application of machine learning and its impact on business and society.
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Machine Learning Rock Star – the End-to-End Practice provides a comprehensive understanding of machine learning. The modules cover deployment, operational success, technical insights, and ethical considerations, offering valuable expertise for business leaders and data scientists.
The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats equips participants to identify potential machine learning deployments generating value for organizations. It also covers reporting on predictive performance and understanding the potential of machine learning.
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership focuses on identifying opportunities for machine learning in various business areas and planning its operational integration. This module also covers forecasting project effectiveness and gaining internal buy-in.
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls provides insights into selecting and evaluating technical approaches for machine learning. It also helps participants interpret predictive models and avoid common technical pitfalls, including screening for bias against protected classes.
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