(UPDATED) Personalized Recommendation Systems using Python | Recommendation Algorithms | Recommendation AI

Create your own personalized recommendation algorithm by understanding how various recommendation algorithm principles work!

(4.7) 42 reviews

1,071 learners

Level Basic

Course period Unlimited

Recommendation System
Recommendation System
Deep Learning(DL)
Deep Learning(DL)
Recommendation System
Recommendation System
Deep Learning(DL)
Deep Learning(DL)

Reviews from Early Learners

Reviews from Early Learners

4.7

5.0

junhkwak

12% enrolled

This course is for intermediate and advanced experts who want to actually implement recommendations. ^^ It's good because it contains content that is not easily seen.

5.0

ajaalsgus

100% enrolled

Thanks to this, my understanding of recommender systems has greatly improved. It's a really great lecture!

5.0

이지호

39% enrolled

I learned the concept of recommendation algorithms from information scattered on various blogs...ㅠㅠㅠㅠ It's nice to have it all organized in one place. I'm about to take a practical class, so I'll work hard!

What you will gain after the course

  • Concepts of recommendation algorithms

  • How various recommendation algorithms work

  • Implementing Recommendation Algorithms Using Python

Implementing a Personalized Recommendation System
Rough but informative with Rough Coding!

The Secret to Successful Services:
Recommendation System Algorithms
👨‍💻

Netflix, Amazon, YouTube, Spotify, and other
world-renowned services, as well as
services utilizing recommendation algorithms,
are increasingly on the rise.

But...
doesn't this sound like your story?

"Can I really grasp the concepts of recommendation algorithms correctly?"
"I understand the concept of recommendation algorithms, but... how do I actually implement them?"

👇👇


Recommendation Systems 📌
With easy and intuitive Python!

As services utilizing recommendation algorithms continue to grow, the number of people wanting to learn about them is also steadily increasing. In line with this trend, <Personalized Recommendation Systems Using Python> aims to explain the accurate concepts and principles of recommendation algorithms using Python, an easy-to-understand and intuitive programming language.

Recommendation algorithms, why Python?

I hope this will be a meaningful time for those who want to learn about recommendation systems and those who wish to develop implementation skills suitable for practical work 😊

💻 Please check the prerequisite knowledge!

  • To take this course, a basic understanding of the Python language and the Numpy, Pandas, and Keras libraries is required.

Learn Differently,
The World of Recommendation Systems
💌

Rough but truly informative!
I am data analyst Rough Coding.


Hello! I am Geochin Coding, currently working as a Data Analyst at "one of the NAVER/Kakao/LINE companies".

Have you been wondering if you can truly grasp the concepts of recommendation algorithms? Do you feel like you understand it when reading a book, but feel completely lost when it comes to actually implementing the algorithm?

Through this course, I intend to solidly establish the foundation of recommendation algorithms by accurately explaining the concepts and operating principles. The course is structured not just to end with simple conceptual explanations, but to provide you with the confidence for actual implementation by coding the specific operating principles together.

Data Analyst, Rough Coding is 👨‍💻

Currently, I am performing data collection, processing, analysis, prediction, visualization, and task automation using Python and visualization tools (Tableau) at "one of the Naver, Kakao, or Line companies".

Key Experience

  • Bachelor of Arts in Statistics, Korea University (Graduated)
  • Korea University Graduate School, Department of Big Data Convergence (Enrolled)
  • QS World University Rankings Evaluator
  • Completed Advanced Artificial Intelligence Course at Korea University SW-Centered University
  • Korea University KUCC (Computer Club) Session Chair
  • Korea University Department Valedictorian (5 times), Overall Valedictorian (1 time)
  • Big Data Analysis Engineer Certification
  • Advanced Data Analytics Semi-Professional (ADsP) Certification
  • Big Data Analysis and Development Blog operation
  • Artificial Intelligence Lecture YouTube Channel Operation

Mentoring in progress

  • Efficient study methods for students dreaming of a career in data analysis
  • Consultation for junior analysts currently working in the field of data analysis
  • Those who are not in an IT role but want to apply IT technology to their own work


Through my knowledge, 
I hope this becomes a lecture 
that we create together.

The beginning is the most important part of everything. If you have any questions while learning, please inquire through [Q&A]. I am also conducting mentoring, so I hope to be of great help to those interested in data analysis. 😊


Unrivaled!
Why this course is special 👍

A vague lecture focused only on simple conceptual explanations?

• The lecture doesn't seem much different from the materials
scattered across the internet.
• The conceptual explanations are good, but
how exactly do I implement it in practice?
• The difficulty level of the language itself is too high.

Systematic curriculum, practice-oriented practical lectures!

• This is a practical lecture focused on
principles and hands-on practice, not just simple conceptual explanations.
• It's not just a collection of web materials, but a compilation of
authoritative reference books.
• It utilizes Python for quick and easy learning.

1️⃣ A practical course focused on principles and practice, not just simple conceptual explanations

Simple conceptual explanations of recommendation algorithms are already scattered across many sites. However, no matter how good a conceptual explanation is, it is useless if it does not lead to an actual, accurate implementation.

In this course, we will not only provide a thorough explanation of recommendation algorithm concepts but also share solid know-how for implementing recommendation systems in real-world business environments.

2️⃣ Lectures using Python for easy and fast learning

The lectures are conducted using Python, a language that can be learned faster than other programming languages and is specialized for the field of artificial intelligence. The course is structured so that you can learn not only the understanding of recommendation algorithms but also the data engineering required for building AI models.

3️⃣ Accurate understanding through a systematic curriculum

Personalized Recommendation Systems using Python (Published by Chungram, written by Im Il)

This is not a lecture hastily put together by simply gathering random information scattered across various websites. The curriculum has been systematically structured based on organized content from authoritative reference materials.


Check out the
learning content 📚

In this course 💻

  • It mainly covers the overall content of personalized recommendation technology.
  • Among them, we specifically cover personalized recommendation technology using continuous values.
  • It also explains Collaborative Filtering, Matrix Factorization, Deep Learning recommendation algorithms, and Hybrid recommendation systems that combine multiple recommendation algorithms.

Orientation

The purpose of this lecture is to understand the working principles of major personalized recommendation algorithms. The objectives and introduction of the lecture are summarized in a 5-minute orientation video, so please check it out via [Course Preview]!

Introduction to Recommendation Systems

We introduce the concept of recommendation systems, which select and present necessary information or products to users based on their past behavioral data or other information, along with various technologies and their evolutionary processes.

  • Main Recommendation Algorithms
  • Recommendation System Application Cases

Basic Recommendation Systems

This is the process of preparing and understanding the basic data for future theory and practice. We will introduce the basic operating principles of recommendation systems.

  • Reading Data
  • Popular Product Method
  • Measuring the Accuracy of Recommender Systems
  • Recommendations by User Group

Collaborative Filtering Recommendation System

We will introduce the concepts and operating principles of similarity-based Collaborative Filtering (CF) and enhance your understanding of these concepts by implementing them together.

  • Principles of Collaborative Filtering
  • Similarity Metrics
  • Basic CF Algorithm
  • Neighbor-based CF
  • Determining the optimal neighborhood size
  • CF considering user rating tendencies
  • Other methods for improving CF accuracy
  • User-based CF and Item-based CF
  • Performance Metrics for Recommender Systems

Matrix Factorization (MF) Based Recommendation

We introduce the concept and operating principles of Matrix Factorization (MF), which is based on matrix operations, and enhance your understanding of the concept by implementing it together.

  • Principles of the Matrix Factorization (MF) method
  • MF algorithm using SGD (Stochastic Gradient Descent)
  • Basic MF algorithm using SGD
  • MF algorithm with train/test split
  • Finding the optimal parameters for MF
  • MF and SVD

Using the Surprise Package

Learn the concepts and operating principles of a package that allows you to easily implement and test CF and MF-based recommendation systems.

  • Basic usage of Surprise
  • Algorithm Comparison
  • Specifying algorithm options
  • Comparison of various conditions
  • Using external data

Recommendation Systems Using Deep Learning

We will enhance our understanding of the concept by exploring the principles and practical applications of operating a recommendation system using artificial neural networks with multiple hidden layers.

  • Converting Matrix Factorization (MF) into a Neural Network
  • Implementing MF with Keras
  • Recommendation Systems Applying Deep Learning
  • Adding variables to deep learning models

Hybrid Recommender Systems

We will enhance understanding through methodological content on complementing and improving mutual performance by combining multiple recommendation algorithms, as well as through practical hands-on exercises.

  • Advantages of Hybrid Recommender Systems
  • Principles of Hybrid Recommender Systems
  • Hybrid Recommendation Systems (Combining CF and MF)

Using Sparse Matrix for processing large-scale data

By learning the methods for processing overwhelming amounts of data and the entire process of applying actual recommendation algorithms, you will gain a sense of practical, real-world skills.

  • The concept of Sparse Matrix and its use in Python
  • Applying Sparse Matrix to Recommendation Algorithms

Issues in building recommendation systems

Through a comprehensive summary of problems and issues that frequently occur when building an actual recommendation system, you will directly learn various know-hows that can help reduce trial and error during the actual production process.

  • New users and items (Cold Start Problem)
  • Scalability
  • Presentation of Recommendations (Presentation)
  • Use of Binary Data
  • Acquiring user's indirect evaluation data (Indirect Evaluation Data)

Check out the instructor's 
Q&A! 💬

Q. Is it mandatory to have prior knowledge of Python, Numpy, Pandas, and Keras?

You must know Python, but you don't need to study the other libraries too deeply in advance. I recommend looking up and studying only the parts you don't know as they come up while taking the course. The library functions used in the lectures are very useful and commonly used, so for those studying for the first time, they could serve as a guideline for what to study first. 😉

Q. Is the data provided in advance?

Of course. The data used in all lectures will be the MovieLens data, which was developed and verified by the GroupLens project at the University of Minnesota. You can download it via the data URL provided before the class begins.

Q. Can I develop an actual recommendation engine by taking this course?

In every lecture chapter, we cover not only the concepts of various recommendation algorithms but also practical hands-on exercises. Therefore, by making slight modifications to the code we practice together to fit your specific domain, you will be able to develop your own recommendation engine.

Q. Do I need to install Python separately or set up a development environment?

There is absolutely no need for that. To eliminate the hassle of installation and setting up a development environment, we will use Colab, a web-based editor provided by Google.

You can find detailed instructions on how to use Colab on my blog, or you can find more information by searching for "how to use Colab" on Google. 

Curious about other courses by Geochin Coding? 📖

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Recommended for
these people

Who is this course right for?

  • Those who are interested in personalized recommendation algorithms

  • Those who want to introduce a recommendation system into their business operations

Need to know before starting?

  • Basic Understanding of Python

  • Basic Understanding of the Numpy Library

  • Basic Understanding of the Pandas Library

  • Basic Understanding of the Keras Library

Hello
This is 거친코딩

7,056

Learners

112

Reviews

102

Answers

4.8

Rating

3

Courses

🙌 Introduction

Hello. I am "Rough Coding," a rough but truly informative data analyst.

  • Korea University, Department of Statistics (Graduated)

  • Korea University Graduate School, Department of Big Data Convergence (Enrolled)

  • QS World University Rankings Evaluation Committee Member

  • Completed the Advanced Artificial Intelligence course at Korea University, a SW-centered university

  • Session Leader of KUCC (Computer Club), Korea University

  • 5-time Department Valedictorian and 1-time Overall Valedictorian at Korea University

  • Big Data Analysis Engineer Certification

  • Advanced Data Analytics Semi-Professional (ADsP) Certification

  • Operating a Big Data analysis and development blog

  • Operating an AI lecture YouTube channel

I am currently working at "one of the NAVER/Kakao companies" performing data collection, processing, analysis, prediction, visualization, and task automation using Python and visualization tools (Tableau).

⭐ Mentoring

  • Efficient study methods for students dreaming of a career in data analysis

  • Mentoring for junior analysts currently working in the field of data analysis

  • Those who are not in an IT role but wish to apply IT technology to their own work.

🌈 Mentoring Process

  • Conducted remotely via Zoom

  • Requirements: Computer, camera, earphones

  • Mentoring will proceed based on pre-prepared questions or your current situation.

🐯 Closing Remarks

  • The beginning is the most important part of everything. Let's make sure to achieve what you want with burning passion!

📨 Email Inquiry

rough_coding@naver.com

More

Curriculum

All

42 lectures ∙ (6hr 14min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

42 reviews

4.7

42 reviews

  • sangjinsu님의 프로필 이미지
    sangjinsu

    Reviews 10

    Average Rating 5.0

    5

    100% enrolled

    This project is about making a recommendation system, and it was very helpful. Thank you.

    • 거친코딩
      Instructor

      It's great news that you're working on a recommendation system project! I'm also working on a recommendation system project in my company, so if you have any questions or want to share anything, please leave a message in the lecture details or in the community section~! Thank you. -Rough Coding Dream-

  • gkstoa06002932님의 프로필 이미지
    gkstoa06002932

    Reviews 3

    Average Rating 5.0

    5

    51% enrolled

    I'm planning to introduce a recommendation system as an in-house service this time, and I think it will be really helpful.

    • 거친코딩
      Instructor

      Comments like this really give me a sense of accomplishment...ㅠ Thanks to this lecture, I will return with a more unique recommendation algorithm lecture in the next lecture. If you have the opportunity to introduce it to your company, I would really appreciate it if you could leave a review about it. -Rough Coding Dream-

  • hodtkqwlf124563님의 프로필 이미지
    hodtkqwlf124563

    Reviews 2

    Average Rating 5.0

    5

    39% enrolled

    I learned the concept of recommendation algorithms from information scattered on various blogs...ㅠㅠㅠㅠ It's nice to have it all organized in one place. I'm about to take a practical class, so I'll work hard!

    • 거친코딩
      Instructor

      As you said, there are many blogs that cover the concept of recommendation algorithms, but there are many that are not continuous or lack information. I hope you can establish a clear concept through this opportunity~! Fighting for practice too!! -Rough Coding Dream-

  • junhkwak님의 프로필 이미지
    junhkwak

    Reviews 2

    Average Rating 4.0

    5

    12% enrolled

    This course is for intermediate and advanced experts who want to actually implement recommendations. ^^ It's good because it contains content that is not easily seen.

    • 거친코딩
      Instructor

      Thank you for leaving a good review. As you said, I tried to make it not just a concept, but only practical content for actual implementation :) This lecture is not the end, and I will come back with more interesting recommended topics. Thank you. -Rough Coding Dream-

  • ajaalsgus님의 프로필 이미지
    ajaalsgus

    Reviews 13

    Average Rating 4.9

    5

    100% enrolled

    Thanks to this, my understanding of recommender systems has greatly improved. It's a really great lecture!

    • 거친코딩
      Instructor

      I'm so happy to hear that you've improved your understanding :) Please continue to work hard for the remaining classes~! If you have any questions, please leave them on the community Q&A board. Thank you. - Rough Coding Dream-

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