Offered By: IBM
Find your Best Bottle of Wine with NLP
Imagine you come into a wine store, and a knowledgeable vintner tells you all that you want to know about their wines and helps you select the best bottle based on your tastes and cravings. Since you had such a good experience you may buy more wine. This may even give you an idea to open an online wine store, based on a recommender system that provides the same recommendations, as the knowledgeable vintner.
Continue readingGuided Project
Machine Learning
203 EnrolledAt a Glance
Imagine you come into a wine store, and a knowledgeable vintner tells you all that you want to know about their wines and helps you select the best bottle based on your tastes and cravings. Since you had such a good experience you may buy more wine. This may even give you an idea to open an online wine store, based on a recommender system that provides the same recommendations, as the knowledgeable vintner.
In this Guided Project, you will use wine dataset and perform some data wrangling techniques to extract interesting information about wines, as well as you will use some Natural Language Processing (NLP) tools to build a recommender system for selecting wines.
A Look at the Project Ahead
- use Hugging Face Transformer model to create embeddings
- create a search function and visual search explorer to select wines
What You'll Need
Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Cloud IDE and Python runtimes that we offer as part of the IBM Skills Network Labs environment. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.
Estimated Effort
30 Min
Level
Intermediate
Industries
Retail
Skills You Will Learn
Data Analysis, Data Science, Embeddable AI, Machine Learning, Python
Language
English
Course Code
GPXX0SV2EN