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AI Development

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Deep Learning & Machine Learning

[Beginner/Introductory] Implementing Recommendation Systems through Various Examples

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

(5.0) 2 reviews

163 learners

  • goodwon5937125
실습 중심
토이프로젝트
추천시스템
AI 코딩
AI 활용법
Machine Learning(ML)
Deep Learning(DL)
PyTorch
AI
LLM

What you will learn!

  • Non-Personalized Recommendation Algorithms: Concept and Implementation

  • Concept and Implementation of Personalized Recommendation Algorithms

  • Operating Principles and Implementation of Recommendation Systems Incorporating Non-Personalization, Personalization Algorithms, and Diversity

Learn about these things

Master the step-by-step recommendation system, from basic to advanced.

  • Considering the learner's level of understanding, the lectures are structured to gradually advance from basic concepts .

  • It is designed to allow for step-by-step learning, starting with basic statistical concepts, moving on to application stages utilizing machine learning, and then to advanced recommendation techniques based on deep learning.

Strengthening practical application of recommendation systems

  • You can learn the core strategies of recommendation systems through a practical approach, covering model training from statistics to deep learning, as well as hybrid techniques and diversity recommendations.

  • It is designed to address a variety of real-world scenarios, such as avoiding user cold-start issues or overly similar recommendations.

  • We have deeply covered the key issues of recommendation systems frequently encountered in practice, strengthening their applicability and problem-solving capabilities in the field.

Check out the learning content

Statistics-based recommendations

  • EDA (Exploratory Data Analysis)

  • View-based recommendations

  • Rating-based recommendations

Content-based recommendations

  • BoW-based recommendations

  • TF-IDF-based recommendations

  • LLM-based recommendations

Machine learning-based recommendations

  • KNN (K-Nearest Neighbors)-based recommendation

  • MF (Matrix Factorization)-based recommendations

Deep learning-based recommendations

  • LightGCN-based recommendation

  • SASRec-based recommendations

Recommended Evaluation Criteria

  • Rating Prediction Evaluation Index

  • Ranking evaluation criteria

  • Diversity Evaluation Index

Hybrid recommendation system

  • Troubleshooting Cold Start Problems

  • Multi-recommended model

I recommend this to these people

software developer

It is actually utilized in various domains such as shopping malls, content platforms, and education services, and if developers and engineers can understand and implement it, it can greatly contribute to enhancing product competitiveness .

Data Scientists and Data Analysts

The ability to comprehensively interpret, model, and evaluate log data, user feedback, and item information will greatly enhance your competitiveness as a data professional.

Marketing Manager

By suggesting the most relevant products or content to individual customers based on user behavior data, you can increase conversion rates, reduce churn , and maximize marketing performance.

Things to note before taking the course

Practice environment

  • Install Chrome browser and create a Google account

  • PC with internet access

Learning Materials

  • Jupyter Notebook files for practice

Recommended for
these people

Who is this course right for?

  • Those interested in the principles and implementation of recommendation systems

  • Those interested in directly implementing a recommendation system, rather than complex formulas or theory-driven explanations.

  • For those wishing to learn recommendation systems via 12 diverse, practical examples.

  • Want to build a diverse recommendation system, not just a simple algorithm.

Need to know before starting?

  • Python, easy for beginners to understand and learn

  • Pandas, a data analysis and processing library

  • Google Colab, cloud-based lab (GPU available)

Hello
This is

안녕하세요, 강의를 맡은 조경원입니다.
저는 중소기업부터 대기업까지 다양한 산업 환경에서 웹 개발, 인공지능(AI), 그리고 AWS 인프라 구축 등 폭넓은 실무 경험을 쌓아왔습니다.

이러한 경험을 바탕으로 2022년부터는 오프라인에서 AI 분야의 강의를 진행하며, 실무와 이론을 연결하는 교육을 이어오고 있습니다.

Curriculum

All

25 lectures ∙ (8hr 59min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

2 reviews

5.0

2 reviews

  • 알맘님의 프로필 이미지
    알맘

    Reviews 1

    Average Rating 5.0

    5

    12% enrolled

    추천시스템에 대해 기초부터 실습까지 체계적으로 배우고 싶어 신청하였습니다. 강의는 추천시스템의 기본 개념(콘텐츠 기반, 협업 필터링 등)부터 최신 딥러닝 기반 방법까지 체계적으로 구성되어 있었고, 실제 코드 실습도 병행되어 이론과 실습을 동시에 익힐 수 있었습니다. 특히, Matrix Factorization과 LightFM, 딥러닝 기반 추천 모델을 직접 구현해보는 과정이 인상 깊었고, Kaggle 실전 예제는 실무에 큰 도움이 되었습니다. 강사님의 설명이 명확하고 실습 코드도 꼼꼼하게 준비되어 있어 부담 없이 따라갈 수 있었습니다. 추천시스템을 처음 배우는 분들이나, 실무 적용을 준비하는 분들에게 강력히 추천합니다!

    • JIYOUNG LEE님의 프로필 이미지
      JIYOUNG LEE

      Reviews 1

      Average Rating 5.0

      5

      60% enrolled

      $29.70

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