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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.

6 learners are taking this course

Level Basic

Course period Unlimited

xgboost
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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 strategies for model validation.

From linear models to the latest ensemble algorithms (LGBM, XGBoost, CatBoost), you will systematically master the algorithms that exert the most powerful influence in the industry. Now, step into the world of AI modeling that predicts 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 Verification: Deep understanding of generalization performance and validation strategies (CV), beyond simple training

* Feature Engineering: Transferring 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: Deriving insights from unlabeled data through clustering, dimensionality reduction, etc.

 

 



 

📱 Curriculum & Project Preview


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

We will outline 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: Machine Learning Overview, Development Environment Setup, Machine Learning Preview


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

We will 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)

This is the process of answering the question, "Is my model truly a good model?" 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)

The heart of machine learning—we delve into various algorithms 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 (Mastering the 3 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 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 "Hanjatong" app
• Developed iPhone "Health Training" app
• Developed content for KT/SK Japanese Namco "Tales of Commons"
• 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
I teach 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. I structure my lectures to explain concepts as easily as possible by incorporating my diverse experiences, and I create examples so that students can apply them to practical exercises. 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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