The Machine Learning for Search training course is intended for software engineers who are interested in incorporating Machine Learning into a scalable search algorithm.
Machine Learning for Search begins by introducing the foundations for each machine learning concept. The course then explores the concept's applicability and limitations. Next, the implementation, use, and specific use cases are explained. Throughout the course, the APIs, along with the theory, will be covered using real-world datasets. This is achieved through a combination of about 30% lecture and 70% lab work. The course concludes with a lesson on applying Deep Learning to improve Search.
Prerequisites:
- Experience using Python integrated with Apache Solr
- A background and basic understanding of machine learning
- Familiarity with TensorFlow, Keras, and Scikit-Learn
- An understanding of seach tools such as Solr and ElasticSearch
Purpose
| Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search |
Audience
| Search Engineers and Machine Learning Practitioners that want to learn more about Machine Learning for Search |
Role
| Data Engineer | Data Scientist | Software Developer | Web Developer |
Skill Level
| Advanced |
Style
| Lecture | Hands-on Activities | Labs |
Duration
| 2 Days |
Related Technologies
| Python | Apache Solr | TensorFlow | Keras | Scikit-Learn |
Â
Learning Objectives
- Demonstrate popular machine learning algorithms, their applicability, and limitations
- Practice the application of these methods in the Search and Machine Learning environment
- Explain practical use cases and limitations of algorithms
