Deep Dive : machine learning and media -building your own recommendation system from scratch
It seems like every tech company is slinging around buzzwords like “big data,” “artificial intelligence,” and “machine learning”. Machine learning is able to make sense of digital data at a much faster rate than any human is capable of doing and hence choosing the application of ML-Recommendation Systems, tends to be a decision of priorities. These systems are personalizing our web experience, telling us what to buy (Amazon), which movies to watch (Netflix), whom to be friends with (Facebook), which songs to listen (Spotify) etc. In this talk I’ll explain the amount of work going behind this, diving into the mechanism of one such way to build these recommendation systems.
After this talk, the audience would be able to understand the actual working of these systems. It involves knowledge of different types of recommendation systems, algorithms used, evaluation of the systems generated, working of deep recommendations – at last eventually building one(model) from scratch.The talk would answer the queries about the domains of the systems created- media, e-commerce, travel & real estate , education , job-boards, etc.- 'how AI has revolutionized e-commerce.' -giving a clear insights to mechanisms responsible for the same.
- Introduction to recommendation systems.
- Domains of recommendation systems.
- Categorising algorithms and their evaluations
- Describing the python libraries used.
- Building a tourism(based on location of user / emotion recognition) /music recommendation system using the libraries – popularity based model & personalised collaborative filtering model.
- Performance analysis of both models & real world instances of recommendation systems.
- Q & A Session
Basic knowledge of machine learning & love for python
Will be updated soon.
Hi, a Computer Science sophomore whose research interests lie in Machine Learning and Artificial Intelligence, occasionally working on blockchain projects. I’m a member of a QS award winning student-led multidisciplinary lab called Next Tech Lab where we research in Artificial Intelligence, Mixed Reality, Internet of Things, Blockchains and Computational Biology. I also regularly participate and give talks in paper-reading sessions and meetups like PyData Amaravati.