Hands-on Introduction to Interpreting Machine Learning Models | Community Webinar

Here is a hands-on introduction to interpreting machine learning models. Interpretable machine learning is needed because machine learning by itself is incomplete as a solution. The complex problems we solve with machine learning can't be solved with traditional software development precisely because we don’t understand all of the problem space we’re trying to solve. By explaining a model’s decisions, we can cover gaps in our understanding of the problem, and increase trust in the products we build with AI. In this session, we will cover the importance of model interpretation and explain various methods and their classifications, including feature importance, feature summary, and local explanations. Delve deeper into machine learning, this playlist will discuss all the help you understand including, what is machine learning, what are the types of machine learning, and more: https://www.youtube.com/playlist?list=PL8eNk_zTBST_stoYSyjMBimtNC3GgxaUA -- At Data Science Dojo, we believe data science is for everyone. Our data science trainings have been attended by more than 10,000 employees from over 2,500 companies globally, including many leaders in tech like Microsoft, Google, and Facebook. For more information please visit: https://hubs.la/Q01Z-13k0 💼 Learn to build LLM-powered apps in just 40 hours with our Large Language Models bootcamp: https://hubs.la/Q01ZZGL-0 💼 Get started in the world of data with our top-rated data science bootcamp: https://hubs.la/Q01ZZDpt0 💼 Master Python for data science, analytics, machine learning, and data engineering: https://hubs.la/Q01ZZD-s0 💼 Explore, analyze, and visualize your data with Power BI desktop: https://hubs.la/Q01ZZF8B0 -- Unleash your data science potential for FREE! Dive into our tutorials, events & courses today! 📚 Learn the essentials of data science and analytics with our data science tutorials: https://hubs.la/Q01ZZJJK0 📚 Stay ahead of the curve with the latest data science content, subscribe to