Building Practical Recommendation Engines - Part 1

[TheJonathan]

Building Practical Recommendation Engines - Part 1
HDRips | MP4/AVC, ~388 kb/s | 1920x1080 | Duration: 02:52:53 | English: AAC, 128 kb/s (2 ch) | 633 MB
Genre: Development / Programming

Make Intelligent predictions with real-world projects

-A step-by-step guide to building recommendation engines in no time
-Get to grips with the best tools available on the market to create efficient recommendation systems
-This hands-on tutorial shows you how to implement different tools for recommendation engines, when to use a particular recommendation engine, and how
A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general.
This video starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, and more. You will get an insight into the pros and cons of different recommendation engines and when to use which recommendation.
With the help of this course, you will quickly get up and running with Recommender systems. You will create recommendation engines of varying complexities, ranging from a simple recommendation engine to real-time recommendation engines.




































































































































































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