We will start by discussing what recommender systems are and what are their applications and benefits.
We will also compare the main techniques of building machine learning models for recommender systems and take a look at metrics and business evaluation techniques.
Finally, we are going to see how to choose these metrics for the required evaluation.
Modin is an early-stage project at UC Berkeley’s RISELab designed to facilitate the use of distributed computing for Data Science.
It is a multiprocess Dataframe library with an identical API to pandas that allows users to speed up their Pandas workflows.
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Loguru is a library which aims to bring enjoyable logging in Python.
Did you ever feel lazy about configuring a logger and used print() instead?… I did, yet logging is fundamental to every application and eases the process of debugging. Using Loguru you have no excuse not to use logging from the start, this is as simple as from loguru import logger.
Also, this library is intended to make Python logging less painful by adding a bunch of useful functionalities that solve caveats of the standard loggers.
Build and Deploy Data Science Products : A Practical Guide to Building a Machine Translation Application This journey is going to be a 8 steps.
In this series we will take a use case, understand the solution landscape and its evolution, explore different architecture choices, look under the hood of the architecture to understand the nuts and bolts, build a prototype, convert the prototype into production ready code, build an application from the production ready code and finally understand the process for deploying the application .
