Recommendation systems allow you to gain insights into data and make a guess on what would be people's preference. It is used all over the web, be it shopping, social networking, or music. This video will teach you how to build unique end-to-end recommendation engines with various tools and enhance your skills.
You will look at various 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 the video. During the course of the video, you will come across creating recommendation engines with R, Python, Apache Spark, Neo4j, Apache Mahout, and more. By the end of the course, you will also learn the best practices and tricks and tips to build efficient recommender systems.
About The Author
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, 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.
Learn to know the flavors of personalized recommenders.
Build content recommendations using another approach, using the Python sklearn, NumPy, and pandas packages.
Use different recommendations to the same person based on their current context
Create context profile of the user.
Learn to provide the capabilities of Spark, such as in-memory distributed computation and fast, easy-to-use APIs.
Learn to build a specific version of Hadoop to access HDFS as well as standard and custom Hadoop input sources.
Know the Matrix Factorization Model and the Alternating Least Squares method.
Learn to build the recommendation engine using Sparksuch as DataFrames, RDD, Pipelines, and Transforms available in Spark MLlib.
Know the actual implementation of the recommendation engine.
Learn to choose the Root Mean Squared Error method to calculate the model accuracy.
Learn to understand the concept of databases and where to apply them.
Learn the cypher query language.
Learn to create nodes and relationships.
Learn how to install Neo4j for Windows.
Learn to download and install Neo4j on the CentOS Linux Platform.
Build recommendation engines using the interface.
Learn to write a query to generate recommendation.
Ability to implement collaborative filtering using Euclidean distance method.
Ability to implement collaborative filtering using cosine similarity.
Learn to setup the Apache mahout software.
Build customized recommender systems that are enterprise-ready, scalable, flexible, and that perform well.
Item-based recommenders recommend similar items to users by considering the similarity between items instead of the similarity between users.
Evaluate the accuracy of the recommender models that we built.
Use of matrix factorization methods to generate model-based recommender implementations in Mahout.
Understand the directions in which recommendation engines are evolving to cope with futuristic situations.
List a few promising use cases that might make you more interested in future of recommendation engines.
Learn to build recommendation engines for improving the robustness and relevance of the recommendations.
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