Learning Path: Build Your Own Recommendation Engines
- 5 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Discover the tools needed to build recommendation engines
- Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations
- Familiarize yourself with machine learning algorithms in different frameworks
- Master different versions of recommendation engines from practical code examples
- Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and others
Learn to install R Package in Rstudio to know how to load and format data
Learn to develop new and different approaches
Learn to know the simple mathematical calculation that is applied between two vectors
The recommenderlab R package is a framework for developing and testing recommendation algorithms used to build recommendation engines. In this video, we’ll see how to installrecommenderlab
Now, we have seen implementations of user-based recommender systems and item-based recommender systems using the R package, recommenderlab. But, can we do the same with Python? In this video, we see theUBCF and IBCF implementations using the Python programming language
If we observe the RMSE values in the model, we can see that the error is a bit higher. The reason may be that we have chosen all the users' rating information while making the predictions. In this video, we will cover finding the top-N nearest neighbors.
Learn to understand the concept of databases and where to apply them.
Learn to setup the Apache mahout software.
Understand the directions in which recommendation engines are evolving to cope with futuristic situations.
- You are required to know basics of data manipulation languages such as Python, R, or similar languages.
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!
Recommender Systems also perform the task of filtering, prioritizing and efficiently delivering relevant information in order to alleviate the problem of information overload, which has created a potential problem to many users.
With all these advantages, Recommendation Engines are very common these days and can be applied in almost every field.
Packt’s Video Learning Paths are an amalgamation of multiple video courses that are logically tied together to provide you with a larger learning curve.
In this Learning Path, you will be introduced to what a recommendation engine is and its applications. You will then learn to build recommender systems by using popular frameworks such as R and Python.
The latter part of the Learning Path will deal with various complex recommendation engines such as personalized recommendation engines, real-time recommendation engines, and 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.
For this Learning Path, we have taken two video courses both authored by Suresh Kumar Gorakala.
Suresh Kumar Gorakala is a Data scientist focused on Artificial Intelligence. He has professional experience close to 10 years, having worked with various global clients across multiple domains and helped them in solving their business problems using Advanced Big Data Analytics. He has extensively worked on Recommendation Engines, Natural language Processing, Advanced Machine Learning, and Graph Databases. He previously co-authored Building a Recommendation System with R for Packt Publishing. He is a passionate traveler and is a photographer by hobby.
- This Learning Path is for anyone who is new to the field of data science and has a basic knowledge of data manipulation languages such as R or Python.