.png&w=1536&q=75)
ML Powered Search
Cohort-based Course
Systematically improve search relevance with learning to rank, active/reinforcement learning, and a bit of vector search
ML Powered Search
Cohort-based Course
Systematically improve search relevance with learning to rank, active/reinforcement learning, and a bit of vector search
Previously at
Course overview
Learn how machine learning improves search relevance
Then use this section to go into more detail about the value students will receive in your course...
Who is this course for
01
ML Engineers optimizing search ranking to measurably improve user search satisfaction
02
Search Practitioners that want to learn to systematically improve search relevance
03
Technology leaders hoping to plan search experiments to maximize clicks and conversions
Prerequisites
Basic Python Programming
Examples will be taught in Python, it's a good idea to have basic Python familiarity under your belt
What you’ll get out of this course
Improve conversions in search
LLMs and human labelers don't understand your users unique needs. But we can learn those subtleties from the clickstream
Develop robust training data
Understand what makes high quality search relevance training data, and how this differs from other domains.
Ranking models from scratch
Learn common ranking models put into production such as LambdaMART and others
Develop effective ranking features
Learn how we systematically develop great ranking features using a combination of vector and lexical search indices
What’s included
Live sessions
Learn directly from Doug Turnbull in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
Week 1
Nov
4
The Search Relevance Problem
Nov
6
Now we train!
Week 2
Nov
10
Creating Great Training Data
Nov
12
Online Feature Exploration with Active Learning
Nov
14
Traction on Search Relevance
Week 3
What people are saying
Trey Grainger
Bertrand Rigaldies
Audrey Lorberfeld
Meet your instructor
Doug Turnbull
Doug Turnbull
Since 2010, Doug Turnbull has worked with companies such as Shopify, Careerbuilder, Wikimedia Foundation, Shipt, and LexisNexis on improving search experiences. Doug coaches and develops search teams from startups to Fortune 500 companies, setting up organizational practices to build relevant search. Doug wrote “Relevant Search” (2016) and co-authored “AI Powered Search” (2022). He also co-created the Elasticsearch Learning to Rank, bringing machine learning to the most popular search engine, which revamped Wikipedia and Yelp, driving intelligent search behind dozens of companies' search experiences.
Course schedule
4-6 hours per week
Tuesdays & Thursdays
1:00pm - 2:00pm EST
If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students
May 7, 2022
Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.
Weekly projects
2 hours per week
Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you