Most websites selling products online show you a list of items that you might be interested in. The better the recommendations the more likely that you will buy any of these, which will increase their sales. But how are these recommendations created?
NASSCOM Center of Excellence, Gurugram along with IBM have organised a hands-on session to build a Machine Learning based interactive recommendation engine using Jupyter Notebooks with IBM Watson Studio, Apache Spark and PixieDust . When combined with Watson Studio and Watson Machine Learning you can quickly produce an interactive dashboard to explore and test a recommendation model.
· Introduction to Machine Learning concepts
· Basics of Recommender Systems
· Understanding types of recommender systems:
- Content based recommender systems
- Collaborative filtering
- User-User collaborative filtering
- Item-Item Collaborative filtering
- Hybrid Methods
· Case Study
· Hands-On (Building a pixie dust based app for recommending shopping cart products)
When: 27th February, Wednesday
10:30am – 2pm
Where: NASSCOM Center of Excellence
Plot No 1, Udyog Vihar Phase 1,
Krishna Balaga is a Watson and Cloud Engineer at IBM. He is a part of Digital Business Group, determined to smoothen the journey for Developers and startups with IBM’s visionary platforms. He has undertaken the roles of an IOT consultant, Computer Vision expert, Cloud Advocate, IOT trainer, Design Automation expert, developing a good amount of street cred while doing so. Currently he’s working on creating novel PoCs demonstrating the cognitive capabilities of IBM Watson and its ability to transform the way businesses operate forever.
· Participants should carry their own charged laptops with good Internet connection.
· Participants are requested to register on IBM Cloud platform using https://ibm.biz/Bd2fLG (Before the workshop)
Key Takeaways for Attendees:
By the end of the session, you will understand how to build a model to provide product recommendations for customers based on their purchase history. You would have created your own notebook, which would include:
· load historical shopping data
· structure and view that data in a table that displays customer information, product categories, and shopping history details
· use the k-means algorithm, which is useful for cluster analysis in data mining, to segment customers into clusters for the purpose of making an in-store purchase recommendation based on shopping history
· deploy the model to the IBM Watson Machine Learning service in IBM Cloud to create your recommendation application
For any queries, please reach out to [masked]
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