DataHour: Introduction to Interpretable Machine Learning

DataHour Recording

Find the resources used in the session HERE

About the DataHour:

Interpretable machine learning is needed because machine learning by itself is incomplete as a solution. The complex problems we solve with machine learning aren't solvable through conventional software engineering. By explaining a model's decisions, we can cover gaps in our understanding of the problem, and its corresponding solution.

Black-box machine learning models are thought to be impenetrable. However, with inputs and outputs alone, a lot can be learned about the reasoning behind their predictions. In this DataHour, we will cover the importance of model interpretation and explain various methods and their classifications, including feature importance, feature summary, and local explanations using Python.

Prerequisites: Basic python and some fundamental idea of Machine Learning.

Who is this DataHour for?

• Students and Freshers with an interest in Data Science.
• Data science professionals who want to accelerate their career growth

Speaker

Serg Masís

Climate & Agronomic Data Scientist at Syngenta

Serg is a data scientist in agriculture with a lengthy background in entrepreneurship and web/app development, and the author of the bestselling book "Interpretable Machine Learning with Python". Passionate about machine learning interpretability, responsible AI, behavioral economics, and causal inference.

You can follow him on Linkedin and Twitter.