This is part 2 of my talk on recommenders. The presentation describes the intuitions of matrix factorization and how to implement it in R. I delivered this in the R-Users Group PH meetup group this November. If you wish to see some more recommender material in this blog, check this out:
With data, I explore the name Cassandra, Zoe, their variants and how they move in popularity over time.
In this video, I’ll discuss my solution to the SIIM-ISIC Melanoma Classification Challenge hosted in Kaggle.
Today I will be taking a look at blood chemistry tests and COVID-19. I am trying to see associations of the different blood cell types when there is a SARS-COV-2 infection. I am joined with my friend Dr. Earl Tiu as I try to explain my findings to him. The data is sourced from Kaggle, […]
Hi all! I’m trying out this new format of publishing my projects through Powerpoint videos. Recording this way is fun. Let me know if this is a better format for you. This pet project is my tribute to books. As a machine learning person, I’d like to see if I can artificially create images of […]
I had the opportunity to again host a Kaggle-like competition. Here is how I would have solved my own problem and how user’s preferences apply in e-commerce.
If a computer would have eyes, what would it be able to recognize? Distinguishing cats and dogs would be nice, but what’s better is recognizing all 7,870 objects in the Open Images dataset!
I’ve been tinkering with customer lifetime value modeling the past few days since the Olist dataset in Kaggle went up. In particular, I wanted to explore the tried and tested probabilistic models, BG/NBD and GammaGamma to forecast future purchases and profits. I also wanted to see if the machine learning approach could do well — […]
Join me in this roadtrip of different recommender algorithms.
With some data wizardry, here’s the TED Talks Network!