Ai Quality Workshop – How To Test And Debug Ml Models

What you'll learn
Rapidly evaluate machine learning models for performance
Identify and address model drift
Debug production ML models
Identify and address possible ML bias issues
Requirements
This course is for data scientists and ML engineers, and assumes a working knowledge of Python and an introductory course in machine learning
Description
Want to skill up your ability to test and debug machine learning models? Ready to be a powerful contributor to the AI era, the next great wave in software and technology?Get taught by leading instructors who have previously taught at Carnegie Mellon University and Stanford University, and who have provided training to thousands of students from around the globe, including hot startups and major global corporations:You will learn the analytics that you need to drive model performanceYou will understand how to create an automated test harness for easier, more effective ML testingYou will learn why AI explainability is the key to understanding the key mechanics of your model and to rapid debuggingUnderstand what Shapley Values are, why they are so important, and how to make the most of themYou will be able to identify the types of drift that can derail model performanceYou will learn how to debug model performance challengesYou will be able to understand how to evaluate model fairness and identify when bias is occurring - and then address itYou will get access to some of the most powerful ML testing and debugging software tools available, for FREE (after signing up for the course, terms and conditions apply)Testimonials from the live, virtual version of the course: "This is what you would pay thousand of dollars for at a university." - Mike"Excellent course!!! Super thanks to Professor Datta, Josh, Arri, and Rick!! :D" - Trevia"Thank you so very much. I learned a ton. Great job!" - K. M. "Fantastic series. Great explanations and great product. Thank you." - Santosh"Thank you everyone to make this course available... wonderful sessions!" - Chris
Overview
Section 1: Welcome! Let's get set up
Lecture 1 Welcome - what you'll get from this course
Lecture 2 How to set up your free TruEra access
Lecture 3 How to use Google Colab for TruEra
Section 2: ML Testing
Lecture 4 Introduction to ML Testing
Lecture 5 Running and Interpreting Tests
Lecture 6 Creating New Tests
Section 3: ML Explainability
Lecture 7 Introduction to ML Explainability
Lecture 8 Overview of Feature Importance Methods
Lecture 9 Shapley Values - Query Definition
Lecture 10 Shapley Values - Comparing Model Outputs
Lecture 11 Shapley Values - Dealing with Feature Interactions
Lecture 12 Shapley Values - Summarization
Lecture 13 Overview - Gradient Based Explanations for Computer Vision
Lecture 14 Design - Gradient-Based Explanations for Computer Vision
Lecture 15 Evaluation - Gradient-Based Explanations for Computer Vision
Lecture 16 Hands-On Learning - Explainability
Lecture 17 Demonstration - Global and Local Explainability Analysis
Section 4: Drift
Lecture 18 Introduction to Drift
Lecture 19 Sources of Drift: Why Does Drift Happen?
Lecture 20 Identifying Drift: Metrics
Lecture 21 Identifying Drift: Challenges
Lecture 22 How to Mitigate Drift
Lecture 23 Hands-on Learning: Drift
Lecture 24 Demonstration - Going from the Model Summary to Drift Analytics
Section 5: ML Performance Debugging
Lecture 25 Introduction to ML Performance Debugging
Lecture 26 ML Peformance Debugging Methodology
Lecture 27 ML Performance Metrics - Classification
Lecture 28 ML Performance Metrics - Regression
Lecture 29 Narrowing Down the Scope of ML Performance Issues
Lecture 30 Hands-On Learning: Performance Debugging
Lecture 31 Demonstration - Performance Debugging
Section 6: Bias and Fairness in Machine Learning
Lecture 32 Introduction to Bias and Fairness in ML
Lecture 33 Worldviews of Fairness in Machine Learning
Lecture 34 How to Pick a Fairness Metric
Lecture 35 How Does Your ML Model Become Unfair?
Lecture 36 Demonstration: Fairness and Bias in ML
Lecture 37 Hands-On Learning: Bias and Fairness in ML
Data Scientists and ML Engineers who are looking to improve their ability to test, evaluate, and debug machine learning models.


Homepage

https://www.udemy.com/course/ai-quality-workshop-how-to-test-and-debug-ml-models/