Implementing Data Science Driven Recommender Systems For Business Applications With R
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH R
What you’ll learn
- Learn what recommender systems are and their importance for business intelligence.
- Learn the main aspects of implementing data science technique within the R Programming Language.
- Implement practical recommender systems using R Programming Language.
- Learn about the theoretical and practical aspects of recommender systems.
Course Content
- Welcome to the Course –> 5 lectures • 17min.
- Basic R Programming –> 11 lectures • 1hr 21min.
- Basic Statistical Concepts Underpinning Recommender Systems –> 9 lectures • 45min.
- What Are Recommender Systems? –> 9 lectures • 47min.
- Miscellaneous Section –> 2 lectures • 9min.
Requirements
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH R
- Are you interested in learning how the Big Tech giants like Amazon and Netflix recommend products and services to you?
- Do you want to learn how data science is hacking the multibillion e-commerce space through recommender systems?
- Do you want to implement your own recommender systems using real-life data?
- Do you want to develop cutting edge analytics and visualisations to support business decisions?
- Are you interested in deploying machine learning and natural language processing for making recommendations based on prior choices and/or user profiles?
You Can Gain An Edge Over Other Data Scientists If You Can Apply R Data Analysis Skills For Making Data-Driven Recommendations Based On User Preferences
- By enhancing the value of your company or business through the extraction of actionable insights from commonly used structured and unstructured data commonly found in the retail and e-commerce space
- Stand out from a pool of other data analysts by gaining proficiency in the most important pillars of developing practical recommender systems
MY COURSE IS A HANDS-ON TRAINING WITH REAL RECOMMENDATION RELATED PROBLEMS- You will learn to use important R data science techniques to derive information and insights from both structured data (such as those obtained in typical retail and/or business context) and unstructured text data
My course provides a foundation to carry out PRACTICAL, real-life recommender systems tasks using Python. By taking this course, you are taking an important step forward in your data science journey to become an expert in deploying the R Programming data science techniques for answering practical retail and e-commerce questions (e.g. what kind of products to recommend based on their previous purchases or their user profile).
Why Should You Take My Course?
I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real-life data from different sources and producing publications for international peer-reviewed journals.
This course will help you gain fluency in deploying data science-based recommended systems in R to inform business decisions. Specifically, you will
- Learn the main aspects of implementing data science techniques in the R Programming Language
- Learn what recommender systems are and why they are so vital to the retail space
- Learn to implement the common data science principles needed for building recommender systems
- Use visualisations to underpin your glean insights from structured and unstructured data
- Implement different recommender systems in the R Programming Language
- Use common natural language processing (NLP) techniques to recommend products and services based on descriptions and/or titlesYou will work on practical mini case studies relating to (a) Online retail product descriptions (b) Movie ratings (c) Book ratings and descriptions to name a few
In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!
ENROLL NOW 🙂