Semantic Search engine using Sentence BERT
- 2.5 hours on-demand video
- Full lifetime access
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- Certificate of Completion
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- Semantic search with BERT
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.
- Begineer and intermediate python developers who are curious about semantic search