Jae-sung Yoon's AI Development Part 2: Mastering Practical Machine Learning Algorithms from A to Z

"Beyond Data Analysis: From Predictive Model Design to Optimization (29 Lectures in Total)" Have you finished your data analysis but feel stuck when it actually comes to building a model? We help you fully master not just how to call libraries, but the working principles of each algorithm and optimal model validation strategies.

(5.0) 3 reviews

31 learners

Level Basic

Course period Unlimited

xgboost
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Reviews from Early Learners

5.0

5.0

이종원

32% enrolled

That's nice

5.0

뒤안길

100% enrolled

It was good for organizing my thoughts.

5.0

Jang Jaehoon

10% enrolled

Thank you for the great lecture!

What you will gain after the course

  • - A sharp eye for objectively evaluating the performance of regression and classification models

  • - Ability to select the optimal algorithm (KNN, SVM, Tree, etc.) suited to data characteristics

  • - Modeling skills to freely handle high-performance ensemble models

  • - Ability to design machine learning pipelines that consolidate complex preprocessing steps into one.

  • - A technique for finding new insights by reducing the dimensions of data or grouping it.

Course Introduction

: "Beyond Data Analysis, from Predictive Model Design to Optimization (Total 29 Lectures)"

Have you finished your data analysis but feel lost when it comes to actually building a model? Beyond simply learning how to call libraries, I will help you master the inner workings of each algorithm and the best model validation strategies.

From linear models to the latest ensemble algorithms (LGBM, XGBoost, CatBoost), you will systematically master the algorithms that demonstrate the most powerful performance in the field. Now, step into the world of AI modeling to predict the future based on analyzed data.

 

 

Key points unique to this course

* Algorithm Deep Dive: Learn over 10 types of algorithms, from basic KNN to the latest boosting models

* Thorough Performance Validation: Deep understanding of generalization performance and validation strategies (CV), beyond simple training

* Feature Engineering: Mastering core feature extraction and preprocessing strategies that determine model performance

* Hyperparameter Tuning: Tuning techniques to find the optimal model, such as GridSearch and RandomSearch

* Mastering Unsupervised Learning: Extracting insights from unlabeled data through clustering, dimensionality reduction, etc.

  



 

📱 Curriculum & Project Preview


✒ Section 1. Introduction and Preparation for Machine Learning (Lectures 1 to 5)

We will draw the lecture roadmap and the big picture of machine learning. After installing the libraries, we will quickly grasp the overall flow by running a simple model ourselves.

Key Learning: Overview of Machine Learning, Setting up the Development Environment, A Taste of Machine Learning


✒Section 2. The Basics of Modeling, Data Preprocessing (Lessons 6 ~ 8)

We reinterpret the content learned during the data analysis stage from a machine learning perspective. We will conduct intensive training on processing data into the optimal format for model learning.

Key Technologies: Feature Engineering, Categorical Data Processing, Scaling

 

✒ Section 3. Evaluation and Validation Strategies (Lectures 9 ~ 14)

"Is my model truly a good model?" This is the process of answering that question. You will learn the strategies used by experts to prevent overfitting and improve generalization performance.

Key Learning: Data Splitting, Regression/Classification Evaluation Metrics, Hyperparameter Tuning, Core Feature Extraction


✒ Section 4. Mastering Supervised Learning Algorithms (Lectures 15 ~ 25)

We will delve into various algorithms, the highlight of machine learning, one by one. Beyond simple theory, you will learn the pros and cons of each algorithm and when to use them.

Core Models: Linear Models, SVM, Decision Trees, Random Forest

Advanced Models: LGBM, XGBoost, CatBoost (Complete mastery of the three major boosting models)


✒ Section 5. Unsupervised Learning and Efficiency (Lectures 26 - 29)

You will learn about unsupervised learning to find hidden structures in data and building pipelines to automate the entire process.

Key Learning: Dimensionality Reduction (PCA), Clustering, Machine Learning Pipeline (Pipeline)


 

✒ Introduction to the Knowledge Sharer

Jaesung Yoon (Lead Instructor of Data Analysis at Like Lion)


Development Experience
• Developed and launched SKT "Island Adventure" mobile content
• Developed and launched KT "Quiz Soccer" mobile content
• Launched SK "Mobile Real Estate Agent"
• Developed iPhone "Hanja-tong" app
• Developed iPhone "Health Training" app
• Developed KT/SK Japanese Namco "Tales of Commons" content
• Developed KT mini-games (Yageum Yageum Land Grab, Aladdin's Magic Lamp, Mystery Block Detective Agency, BUZZ and BUZZ)

Teaching Experience
A veteran instructor with 19 years of experience in teaching and development for current employees of famous domestic companies and job seekers, including Samsung Multi Campus, Busan IT Industry Promotion Agency, Jeonju IT & Cultural Industry Promotion Agency, Incheon IT Industry Promotion Agency, Korea Radio Promotion Association, SK C&C, T Academy, Korea Institute for Robot Industry Advancement, Daejeon ETRI, Samsung Electronics, nica Education Center, Korea Productivity Center, Hanwha S&C, LG Electronics, and more.

Teaching Fields
Teaches in fields such as Java, Android, Frameworks, Databases, UML, iPhone, Big Data processing and analysis, Python, IoT, data analysis using R/Python, Deep Learning, Machine Learning AI, and Spark. The lectures are structured to explain concepts as easily as possible by incorporating diverse experiences, and to create examples that can be applied to practice. Since this is not an offline class, please use the Q&A for anything you don't understand. I will make sure to resolve your questions.

Recommended for
these people

Who is this course right for?

  • - For those who want to step into the world of full-scale AI modeling: Recommended for those who have completed the basics of data analysis (Numpy, Pandas) but feel lost on how to use them to create artificial intelligence models that predict the future.

  • - Those who need 'selection criteria' for algorithms: This is an essential course for aspiring data scientists who want to clearly compare the pros and cons of various machine learning models and understand which model to use in specific situations.

  • - For those who want to master the latest ensemble models: Highly recommended for those who want to go beyond Random Forest and learn the practical applications of LGBM, XGBoost, and CatBoost, which are essential in both the industry and competitions (such as Kaggle).

  • - For those who want to push model performance to the limit: This is suitable for those who need intermediate techniques to maximize the 'generalization performance' of a model through hyperparameter tuning and validation strategies (CV), rather than just simple implementation.

  • - For those who want to automate machine learning workflows: Recommended for current developers and analysts who want to acquire the ability to build pipelines that connect complex processes from data preprocessing to model evaluation into a single workflow.

Need to know before starting?

  • Basic knowledge of Python syntax and data analysis libraries (NumPy, Pandas) is required.

  • If you are unfamiliar with data handling, we recommend taking the [Required Prerequisite] Python Data Analysis Master course first.

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  • jjhgwx님의 프로필 이미지
    jjhgwx

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    5

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    Thank you for the great lecture!

    • dachki님의 프로필 이미지
      dachki

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      100% enrolled

      It was good for organizing my thoughts.

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