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How Strong is Your Machine Learning Model?

Luke Lin
Instructor at NYC Data Science Academy
Panelist Spotlight

Luke obtained his doctorate in Mathematics at Stony Brook University, where he concentrated in partial differential equations. As a lifelong learner of mathematics, he is extremely efficient in quantitative analysis and also skilled at communicating abstract concepts. Being extremely passionate to share the insight of data from various industries. Luke looks forward to meeting curious students from all. kinds of backgrounds enrolling at the Academy.

How Strong is Your Machine Learning Model? on April 8th, 2021

Machine learning enables a system to learn from data instead of explicit programming. It is a form of AI that can produce predictions as a predictive algorithm trains input data. As data science professionals, we must ensure that our models will provide accurate predictions at best, so we develop ways to monitor our predictive model's performance at every stage.

Learn about the concept of the likelihood function in logistic regression. A likelihood function is a widely accepted concept for such measurement, from a simple linear model such as logistic regression to a sophisticated neural network.

Moreover, you will understand that a model can be taken for a family of parameterized distributions. This idea will also be generalized to a simple linear regression to introduce the likelihood approach to train a model.

This workshop is best for professionals interested in learning more about the practical applications of AI and machine learning.

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