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Machine Learning for Search

Course Summary

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 50% lecture and 50% lab work. The course concludes with a lesson on applying Deep Learning to improve Search.

The Machine Learning for Search course is taught using Python language integrated with Solr. Students should have some background with Machine Learning, familiarity with TensorFlow, Keras, Scikit-Learn, and a knowledge of Search such as Solr and/or ElasticSearch.

Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.
Search Engineers and Machine Learning Practitioners that want to learn more about Machine Learning for Search.
Data Engineer - Data Scientist - Software Developer - Web Developer
Skill Level
2 Days
Related Technologies
Python | Apache Solr


Productivity 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.

What You'll Learn:

In the Machine Learning for Search training course, you'll learn:
  • Vector Space Embedding
    • Beyond TFIDF
    • Keras Embedding Layers
    • Word2Vec, CBOW and Skip-Gram Architecture
    • Doc2Vec and Paragraph Vectors
  • Synonym Generation as an ML Problem
    • Conventional Synonym Generation Methods
    • AI-based Synonym Generation
    • How Should Synonyms be Used with Search?
  • Search Ranking
    • Conventional Methods of Search Ranking in Solr
    • Using AI-based Vector Embedding to Improve Search Ranking
  • Named Entity Recognition (NER) and Search
    • SpaCy and NER Pipelines
    • Integrating NER with Deep-Learning/Keras
    • NER and Search
  • Keras API and Layers
    • Introducing Keras API in TensorFlow
    • Understanding Layers
    • Overview of Neural Network Architectures
      • RNN/LSTM/GRU
      • Etc
  • Applying Deep Learning to Improve Search
    • BOW/n-gram Representation vs. Deep Meaning
    • BERT
    • Self-Attention and Transformer Architecture
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


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