Practical Recommender Systems For Business Applications in R
Practical Recommender Systems For Business Applications in R, available at $64.99, has an average rating of 4.5, with 36 lectures, based on 17 reviews, and has 1153 subscribers.
You will learn about 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 This course is ideal for individuals who are People Wanting To Master The R Programming Language For Data Science or Students Interested In Developing Powerful Data Visualisations or Learning to Make Product and Service Recommendations Based on Prior Choices or Identify the Best Recommender System For Your Problem It is particularly useful for People Wanting To Master The R Programming Language For Data Science or Students Interested In Developing Powerful Data Visualisations or Learning to Make Product and Service Recommendations Based on Prior Choices or Identify the Best Recommender System For Your Problem.
Enroll now: Practical Recommender Systems For Business Applications in R
Summary
Title: Practical Recommender Systems For Business Applications in R
Price: $64.99
Average Rating: 4.5
Number of Lectures: 36
Number of Published Lectures: 36
Number of Curriculum Items: 36
Number of Published Curriculum Objects: 36
Original Price: $94.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH R
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Are you interested in learning how the Big Tech giants like Amazon and Netflix recommend products and services to you?
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Do you want to learn how data science is hacking the multibillion e-commerce space through recommender systems?
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Do you want to implement your own recommender systems using real-life data?
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Do you want to develop cutting edge analytics and visualisations to support business decisions?
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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
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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
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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
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Learn the main aspects of implementing data science techniques in the R Programming Language
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Learn what recommender systems are and why they are so vital to the retail space
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Learn to implement the common data science principles needed for building recommender systems
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Use visualisations to underpin your glean insights from structured and unstructured data
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Implement different recommender systems in the R Programming Language
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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 🙂
Course Curriculum
Chapter 1: Welcome to the Course
Lecture 1: What Is the Course About?
Lecture 2: Data and Code
Lecture 3: Install R and RStudio
Lecture 4: Different Data Types
Lecture 5: Why Recommender Systems?
Chapter 2: Basic R Programming
Lecture 1: Read CSV and Excel Data
Lecture 2: Read in Data from Online HTML Tables-Part 1
Lecture 3: Read in Data from Online HTML Tables-Part 2
Lecture 4: Data Cleaning
Lecture 5: More Data Cleaning
Lecture 6: Pre-processing Tasks and the Pipe Operator
Lecture 7: DPLYR-1
Lecture 8: DPLYR-2
Lecture 9: Some Joining
Lecture 10: The Tall and Short Of It
Lecture 11: Visualize Ratings
Chapter 3: Basic Statistical Concepts Underpinning Recommender Systems
Lecture 1: Principal Components Analysis (PCA)-Theory
Lecture 2: Implement PCA in R
Lecture 3: Single Vector Decomposition (SVD)- Theory
Lecture 4: Implement SVD in R
Lecture 5: Unsupervised Learning-Theory
Lecture 6: k-Means Clustering-Theory
Lecture 7: K-Means Implementation
Lecture 8: Supervised Learning-Theory
Lecture 9: Cosine Similarity
Chapter 4: What Are Recommender Systems?
Lecture 1: Different Types of Recommender Systems
Lecture 2: The Recommenderlab Package
Lecture 3: Prepare Your Data For Use in Recommenderlab
Lecture 4: A Simple Cosine Similarity Based Recommender Engine
Lecture 5: Explore Other Recommenderlab Models
Lecture 6: Collaborative Filtering With Cosine Similarity
Lecture 7: Clustering For Identifying Similar Books
Lecture 8: Identify Top Reader Preferences
Lecture 9: Item Based Recommendations
Chapter 5: Miscellaneous Section
Lecture 1: Using R Within Colab
Lecture 2: What Are Wordclouds?
Instructors
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Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 2 votes
- 5 stars: 14 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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