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2022-04-28T22:25:33Z

DevelopmentDatabase Design & DevelopmentR (programming language)

Practical Recommender Systems For Business Applications in R

Implementing Data Science Driven Recommender Systems For Business Applications With R
New
Rating: 4.8 out of 54.8 (5 ratings)
26 students
Created by Minerva Singh
Last updated 4/2022
English
English [Auto]

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

Requirements

  • Be Able To Operate & Install Software On A Computer
  • Prior Exposure to R Programming Concepts Will be Helpful
  • Prior Exposure to the R Studio Environment
  • An Interest in Learning About Practical Recommender Systems

Description

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 titles


    You 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 :)

Who this course is for:

  • People Wanting To Master The R Programming Language For Data Science
  • Students Interested In Developing Powerful Data Visualisations
  • Learning to Make Product and Service Recommendations Based on Prior Choices
  • Identify the Best Recommender System For Your Problem

Instructor

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)
Minerva Singh
  • 4.5 Instructor Rating
  • 16,498 Reviews
  • 83,356 Students
  • 47 Courses

I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.

I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).

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