░ ░▒▒▒▓▓█████▌░░ University of OXford & University of camBRiDGE ░░▐████▓▓▒▒▒░ ░
▄▄██▓▄ ▐███▌ ████▐███████▓▄ █████████▄ ▐███▌██████▓▄▄ ▄▄▓████▓ ▐████████
▄████████████ ▐███▌▐███▌▀████▌████▌ ▀███▓ ▀▀▀ ███▓▀▀████ ▄▓███▀ ▐███▓▀▀▀
▐███▀ ▀███▀███▓▄███▀ ▐███ ▄███▀▐███▓ ▄████▀▐███▌███▓ ███▓████ ▓███▌
███▌ ▐██▌ ▀████▀ ▓███████▄▄▐████████▓▄ ▐███▓███▓ ▐██████▌ ▀█████▓██████▓
████▄ ▄███▄▄███▀███▓ ████ ▀███▓███▓ ▀████▌████▐███▌ ███████▓ ▓██████▌
▐████████▀████▌ ███████▌ ▄███████▌ ████████▓███▌▄▄███▓▀█████▄▄████▓███▄▄▄▄▄
▀▀▓██▀▀ ▐████ ▐███▓████████▓▀████▌ ▐███████████████▓▀▀ ▀▀▓████▀▀░▓███████▌
...is a collective term for characteristics that the two institutions share.
·-- PUBLISHED -----·> Packt Publishing
·-- LECTURESHIP ---·> Learning Path: Expert Python Projects
·-- LECTURE DATE --·> 05-2017
01-2017 {Published}
·-- LEVEL ---------·> [ ] Starting {Beginner|Newcomer}
[■] Progressing {Intermediate|Advanced}
·-- PERIOD in HRS--·> 18+
·-- SCALE ---------·> 78x50mb
·-- LECTURE LINK --·> https://goo.gl/G2ZCxb
This path navigates across the following products
(in sequential order):
Building Practical Recommendation Engines û Part 1 (2h 52m)
Building Practical Recommendation Engines û Part 2 (2h 12m)
With the progress in time, we do not have to rely on crystal balls any
more to predict the future, we have data! Recommender systems or
Recommendation Engines serve as the modern-day crystal balls, with the
exception that all of the predictions made by them are backed by data!
Recommendation Engines are very common these days and can be applied in
a variety of applications.
In this Learning Path, you will be introduced to what a recommendation
engine is, its applications. You will then learn to build recommender
systems by using popular frameworks such as R, and Python.
The later part of the Learning Path, will deal with various complex
recommendation engines such as personalized recommendation engines,
real-time recommendation engines, SVD recommender systems. You will also
get a quick glance into the future of recommendation systems.
By the end of this Learning Path, you will be able to build efficient
recommendation engines by following the best practices.