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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.

(4.6) 5 reviews

250 learners

Level Basic

Course period Unlimited

  • goodwon5937125
Machine Learning(ML)
Machine Learning(ML)
Deep Learning(DL)
Deep Learning(DL)
PyTorch
PyTorch
AI
AI
LLM
LLM
Machine Learning(ML)
Machine Learning(ML)
Deep Learning(DL)
Deep Learning(DL)
PyTorch
PyTorch
AI
AI
LLM
LLM

What you will gain after the course

  • 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

379

Learners

11

Reviews

2

Answers

4.8

Rating

3

Courses

Hello, I am Gyeongwon Cho, your instructor.
I have built extensive practical experience across various industrial environments, from SMEs to large corporations, in fields such as web development, artificial intelligence (AI), and AWS infrastructure construction.

Based on this experience, I have been conducting offline lectures in the field of AI since 2022, providing education that bridges the gap between practical application and theory.

Curriculum

All

25 lectures ∙ (8hr 59min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

5 reviews

4.6

5 reviews

  • tkrhd47159123님의 프로필 이미지
    tkrhd47159123

    Reviews 3

    Average Rating 5.0

    5

    100% enrolled

    • jjhgwx님의 프로필 이미지
      jjhgwx

      Reviews 644

      Average Rating 4.9

      5

      12% enrolled

      Thank you for the great lecture!

      • witwayy5756님의 프로필 이미지
        witwayy5756

        Reviews 1

        Average Rating 5.0

        5

        60% enrolled

        • haduri295712님의 프로필 이미지
          haduri295712

          Reviews 1

          Average Rating 5.0

          5

          12% enrolled

          I applied because I wanted to systematically learn about recommendation systems, from basics to practical application. The lectures were systematically structured, covering everything from fundamental concepts of recommendation systems (content-based, collaborative filtering, etc.) to the latest deep learning-based methods. Practical coding exercises were also included, allowing me to grasp both theory and practice simultaneously. In particular, the process of directly implementing Matrix Factorization, LightFM, and deep learning-based recommendation models was very impressive, and the Kaggle practical examples were a great help for real-world applications. The instructor's explanations were clear, and the practice code was meticulously prepared, making it easy to follow along. I highly recommend this to those who are new to recommendation systems or those preparing for practical application!

          • twoj님의 프로필 이미지
            twoj

            Reviews 31

            Average Rating 4.9

            3

            100% enrolled

            $29.70

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