Explaining Machine Learning Models - Deep Dive
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Explaining Machine Learning Models - Deep Dive

  • Get insider practical knowledge on how to get the best out of various model explainability techniques.

Created by Selva Prabhakaran

  • 11 Video Lessons

  • English

  • English captions

What you will learn

01

Understanding Model Interpretability

02

Math behind Explainability techniques: SHAP, LIME, Tree Interpreter, PDP

03

Interpreting Complex Models

04

Which features are harming for your classification models

05

Identifying few vital causes

06

Case Studies and Real-world Applications with full python workout

Course Curriculum

Requirements

  • Courses Page1 Basics of Python
  • Courses Page1 Foundational knowledge of Data Science
  • Courses Page1 High school maths

Who should attend this course?

  • Data Science Aspirants

  • Data Science Professionals

  • Software/Data engineers interested in quantitative analysis

  • Professionals working with large datasets

  • Data analysts, economists, researchers

Instructor

Selva Prabhakaran Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this project!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I’ve put up the series of courses and projects with detailed explanations – just like an on the job experience. Hope you love it!

  • 4.5+Instructor rating

  • 200+ reviews

  • 75K+students

  • 50+ Courses

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