Predict the future by making the most of the data you have today!
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.
Prerequisites: The knowledge of the data science concepts is beneficial.
Resources: Code downloads and errata:
This path navigates across the following products (in sequential order):