// Learning To Rank training
What is Learning to Rank and How to Build your Training Set
With LTR becoming more and more popular, organizations struggle with the problem of how to collect and structure relevance signals necessary to train their ranking models.
This training is a technical guide to exploring and mastering various techniques to generate your training set(s) correctly and efficiently.
Recorded
£ 150,00
If you are not able to attend public training, this is the best option for you. You will be able to take the course at your own pace and rhythm and learn whenever it fits your schedule and mood.
- Top expert trainers
- Q&A by e-mail
- Certificate of Attendance
Public classroom
£ 250,00
SCHEDULE
29th February 2024
3:00 – 7:00 PM GMT
- Live on Zoom
- Top expert trainers
- Certificate of Attendance
Private
ask for quote
If you are looking for intensive sessions tailored to your (or your team’s) experience, then private training is your perfect choice!
- In presence or Online
- Tailored Training
- Top Expert Trainers
- Certificate of Attendance
Recorded
£ 150,00
If you are not able to attend public training, this is the best option for you. You will be able to take the course at your own pace and rhythm and learn whenever it fits your schedule and mood.
- Top expert trainers
- Q&A by e-mail
- Certificate of Attendance
Private
ask for quote
If you are looking for intensive sessions tailored to your (or your team’s) experience, then private training is your perfect choice!
- In presence or Online
- Tailored Training
- Top Expert Trainers
- Certificate of Attendance
Based on experience with leading companies including
Based on experience with leading companies including
// feedback
PREREQUISITES
• Basic understanding of Search Engines and Machine Learning
WHAT YOU WILL LEARN
• Model and collect the necessary feedback from the users (implicit or explicit)
• Calculate for each training sample a relevance label that is meaningful and not ambiguous (Click Through Rate, Sales Rate…)
• Transform the raw data collected into an effective training set (in the numerical vector format most of the LTR training libraries expect)
INTENDED AUDIENCE
• Technical Managers
• Data scientists
• Software Engineers
• Developers
• Machine Learning passionates
Your Trainers
Alessandro Benedetti
APACHE LUCENE/SOLR COMMITTER
APACHE SOLR PMC MEMBER
Alessandro has been involved in designing and developing search-relevant solutions from 2010.
Over the years he has worked on various projects, with various open source technologies aiming to build search solutions able to satisfy the user information needs, often integrating such solutions with machine learning and artificial intelligence technologies.
Anna Ruggero
R&D Search Software Engineer, her focus is on the integration of Information Retrieval systems with advanced Machine Learning, Natural Language Processing and Data Mining algorithms. She likes to find new solutions that integrate her work as a Search Consultant with the latest Academia studies.
The schedule
The training session is meticulously structured for optimal comprehension and retention.
It commences with a comprehensive 1-hour theory session, followed by a brief 15-minute test to assess understanding. Subsequently, participants engage in a 45-minute Q&A segment, fostering interactive learning and clarification of concepts. The training then resumes with another 1-hour theory module, another 15-minute evaluation test, and concludes with a final 45-minute Q&A session to consolidate knowledge and address any remaining queries.
3:00-4:00 theory session
4:00-4:15 test
4:15-5:00 Q&A segment
5:00-6:00 theory session
6:00-6:15 test
6:15-7:00 Q&A segment
Topics
1. INTRODUCTION TO LEARNING TO RANK
The training begins by providing participants with a foundational understanding of Learning To Rank (LTR). This introductory module offers insights into the significance, applications, and fundamental concepts underlying LTR, setting the stage for deeper exploration.
2. Offline Learning To Rank (approaches and algorithms)
Following the introduction, the focus shifts to Offline Learning To Rank. Participants delve into various approaches and algorithms, understanding the mechanics, advantages, and limitations of each. Through real-world examples and case studies, learners gain practical insights into implementing these techniques effectively.
3. Online Learning To Rank (algorithms and state of the art)
Building on the offline concepts, the training then transitions to Online Learning To Rank. This module explores advanced algorithms and delves into the cutting-edge developments in the field. Participants will grasp the nuances of online ranking scenarios, algorithmic strategies, and emerging trends shaping the future of LTR.
4. Create a Training Set (feature engineering and relevance estimation)
A pivotal component of the training involves hands-on experience in crafting a training set. Participants will learn the intricacies of feature engineering, understanding how to select, extract, and transform relevant features for optimal ranking performance. Additionally, the module covers relevance estimation techniques, ensuring that the training data accurately represents the desired ranking outcomes.
5. Learning To Rank Metrics
The efficacy of LTR models hinges on appropriate evaluation metrics. This segment acquaints participants with a range of metrics tailored for assessing ranking quality. From precision-recall curves to NDCG (Normalized Discounted Cumulative Gain), learners will gain proficiency in selecting and interpreting metrics that align with specific application requirements.
6. Create a Test Set for Evaluation
To culminate the training, participants learn the critical task of creating a test set for evaluation purposes. This module enables them to see how to apply the acquired knowledge, validate model performance, and fine-tune ranking algorithms, ensuring robustness and reliability in real-world scenarios.
FAQ
The training session is conducted online via Zoom for live participation. However, for those who may not be able to attend in real-time or prefer to learn at their own pace, we also offer the option to purchase a recorded version. This allows participants to access the content online and review it at their convenience.
Absolutely! Everyone is welcome to participate in this training. Please note that there are specific prerequisites listed nearby, so be sure to check those before inviting others to ensure they meet the requirements.
The ‘Learning To Rank: Introduction and How to Build Your Training Set’ training session has a duration of 4 hours. This includes a 2-hour theory session, a 30-minute test, followed by a 1.5-hour Q&A segment.
Yes, absolutely! You will have the opportunity to ask any questions you may have at the end of the test module.
Your teachers will be:
– Alessandro Benedetti, Apache Lucene/Solr committer and Apache Solr PMC member.
– Anna Ruggero, R&D Software Engineer @ Sease
Yes, indeed! At the conclusion of the training, you will receive a certificate of attendance via email.
If you need it, you can consult our Training’s Terms and Conditions.
Yes, we do offer corporate training sessions. Feel free to reach out to us, and we’ll be happy to discuss the best options tailored for you and your team!