Semantic Search engine using Sentence BERT

Semantic Search engine using Sentence BERT

Course Description

Learn to build semantic search engine detection engine with sentence BERT

Build a strong foundation in Semantic Search with this tutorial for beginners.

• Understanding of semantic search

• Learn word embeddings from scratch

• Learn limitation of BERT for sentences

• Leverage sentence BERT for finding similar news headlines

• Learn how to represent text as numeric vectors using sentence BERT embeddings

• User Jupyter Notebook for programming

• Build a real life web application or semantic search

  
  

• A Powerful Skill at Your Fingertips  Learning the fundamentals of semantic search puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation.

No prior knowledge of word embedding or BERT is assumed. I'll be covering topics like Word Embeddings , BERT , Glove, SBERT from scratch.

Jobs in semantic search systems area are plentiful, and being able to learn it with BERT will give you a strong edge. BERT is  state of art language model and surpasses all prior techniques in natural language processing.

Semantic search is becoming very popular. Google, Yahoo, Bing and Youtube are few famous example of semantic search systems in action.  Semantic search engines are vital in information retrieval .  Learning semantic search with SBERT will help you become a natural language processing (NLP) developer which is in high demand.

Content and Overview  

This course teaches you on how to build semantic search engine using open source Python and Jupyter framework.  You will work along with me step by step to build following answers

• Introduction to semantic search

• Introduction to Word Embeddings

• Build an jupyter notebook step by step using BERT

• Build a real world web application to find similar news headlines

What am I going to get from this course?

• Learn semantic search and build similarity search engine from professional trainer from your own desk.

• Over 10 lectures teaching you how to build similarity search engine

• Suitable for beginner programmers and ideal for users who learn faster when shown.

• Visual training method, offering users increased retention and accelerated learning.

• Breaks even the most complex applications down into simplistic steps.

• Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Learn how to use Sentence BERT to find similar news headlines

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What you will learn

• Semantic search with BERT

Rating: 3.95

Level: Beginner Level

Duration: 2.5 hours

Instructor: Evergreen Technologies