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Kaggle Advanced Machine Learning Practical Crash Course

This course is designed to upgrade your skills as a practical machine learning development expert by implementing the machine learning model of the Home Credit Default Risk competition on Kaggle.

(4.9) 71 reviews

1,473 students

Machine Learning(ML)
Kaggle

This course is prepared for Intermediate Learners.

What you will learn!

  • Upgrade your practical machine learning implementation skills by tackling Kaggle's practical competition problems

  • Upgrade implementation capabilities to the level where machine learning can be applied in practice

  • How to improve the performance of machine learning models

  • Improving Data Analysis Skills for Machine Learning

  • Specific implementation methods of machine learning feature engineering

Machine learning, with practical implementation capabilities!
Implement your own contest machine learning model.

Lecture Introduction 🤖

hello,

I am Cheolmin Kwon, author of The Complete Guide to Python Machine Learning.

To become a true machine learning expert needed in practice, you need to have not only an understanding of machine learning, but also the ability to process data and understand the application tasks. However, these abilities are difficult to obtain even if you put in a lot of time and effort if you do not have actual experience or are not trained systematically.

This time, the "Kaggle Advanced Machine Learning Practical Challenge" course was created to help you cultivate these three elements while implementing Kaggle's "Home Credit Default Risk Competition" machine learning problem together, and to upgrade your practical machine learning implementation skills and confidence.

권 철민, 캐글(Kaggle) 머신러닝

The 'Home Credit Default Risk Competition' problem has a data model and several data sets that can be used in practical work.

This lecture will explain in detail and implement code based on the problems of this competition so that you can sufficiently cultivate the relevant capabilities in the important areas of machine learning such as data models, analysis domains, data analysis EDA, feature engineering, hyperparameter tuning, and model performance optimization .

The machine learning algorithm used in the lecture is LightGBM, which is loved by many Kagglers. Through the implementation task, you will write an implementation code that is in the top 10% of the Home Credit Default Risk competition, and through this, you will be able to gain confidence in implementing a model that optimizes performance.

Features of this course 📚

1. Improved understanding of actual implementation through detailed and easy-to-understand practical code explanations and Live Coding

Most of the lectures are about explaining practical codes, and I will explain the codes line by line in great detail. In particular, for the important implementation parts, I will make it so that you can do Live Coding with me, which will further improve your understanding of the implementation.

2. Improved ability to implement performance-oriented models in preparation for competitions such as Kaggle and Deacon

In this course, you will learn advanced machine learning techniques, feature engineering, and hyperparameter tuning techniques to help you achieve high scores in competitions such as Kaggle and Deacon.
This will get you to a level where you can confidently take on machine learning competitions.

3. Detailed explanation of all areas of machine learning required in practice

This course provides detailed explanations of all areas of machine learning, including data models and analysis domains, data analysis EDA, feature engineering, hyperparameter tuning, and model performance optimization.
Through this, you will be able to improve not only machine learning but also data processing and business domain understanding capabilities, thereby laying the foundation for becoming a machine learning expert required in the field.

Lecture Player Knowledge 🏃‍♂️

Python Machine Learning Complete Guide Inflearn Bestseller
Meet the popular dinosaur book in video!

This lecture is an Advanced Machine Learning Project lecture for students who have a basic understanding of machine learning. It is designed assuming that you understand the contents of Chapters 1 to 4 (Classification) of the book ' The Complete Guide to Python Machine Learning ' .

Even if you have not read the book or lecture ' The Complete Guide to Python Machine Learning ', you can take the course if you have previewed the table of contents of 'The Complete Guide to Python Machine Learning' and are familiar with the contents of the table of contents up to Chapter 4.

Practice Environment 💻

Jupyter Notebook Colab

Any environment with more than 12GB of RAM memory is possible. (8GB or so may be difficult to practice due to insufficient memory in the final practice stage.) If you do not have more than 12GB of RAM, you can create a server using Google Cloud's $300 free credit or use Google Colab. The first section of the lecture explains in detail how to set up these practice environments.

The practice code is provided in the form of a Jupyter notebook, and the practice code for Google Colab is provided separately. The practice code and lecture materials can be downloaded from the lecture session materials room.

People I met at Inflearn 👨‍💻

Read the interview with Kwon Chul-min | Go to

Recommended for
these people!

Who is this course right for?

  • Those who are seriously challenging Kaggle or Datacamp

  • Those who are wondering how to apply machine learning to their work

  • Those who want to implement practical models beyond understanding machine learning

  • Those who want to upgrade their machine learning skills

  • If you need to improve the performance of your machine learning model

  • Those who want to improve their practical machine learning skills by tackling difficult problems

Need to know before starting?

  • Understanding Python Machine Learning

  • Python and Pandas implementation skills

Hello
This is dooleyz3525

25,070

Students

1,183

Reviews

3,919

Answers

4.9

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(전) 엔코아 컨설팅

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파이썬 머신러닝 완벽 가이드 저자

Curriculum

All

70 lectures ∙ (12hr 55min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

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71 reviews

4.9

71 reviews

  • 막김님의 프로필 이미지
    막김

    Reviews 12

    Average Rating 4.8

    5

    100% enrolled

    권철민 선생님 강의는 무조건 별 5개

    • Idea님의 프로필 이미지
      Idea

      Reviews 15

      Average Rating 4.9

      5

      100% enrolled

      실제 캐글 데이터를 사용하며 실전 감각을 익힐 수 있는 좋은 강의인 것 같습니다. 권철민님 강의를 처음 접한게 "머신러닝 완벽 가이드"라는 강의를 들을 때였는데, 이번 강의도 역시 너무 좋네요... 항상 좋은 강의 만들어주셔서 너무 감사드리고, 무엇보다 제가 진짜 마음에 들었던 부분은 Q&A를 올리면 빠른 시간내에 답변을 달아주신다는 점...이 부분이 굉장히 좋은 point가 아닐까 싶습니다. 물론 강의 퀄리티는 말할 것도 없구요. 다시 한 번 감사드립니다.

      • 문송합니다님의 프로필 이미지
        문송합니다

        Reviews 2

        Average Rating 5.0

        5

        6% enrolled

        pandas를 어느정도 다룬다고 생각했는데, 단순히 할줄 안다고 성능이 나오는게 아니더라고요. 복잡한 데이터 세트에서 머신러닝 알고리즘을 어떻게 적용해야 하는지, 그리고 데이터 전처리, 분석 도메인에 대한 중요성을 알려준 고마운 강의 입니다. 이걸 들으니 캐글에서 왜 순위가 안 올라가는지 알것 같아 현타가 옵니다. 올해 상금도 빵빵하던데...

        • 양문일님의 프로필 이미지
          양문일

          Reviews 9

          Average Rating 5.0

          5

          100% enrolled

          좋은강의항상감사합니다. 이번강의역시최고네요

          • 런닝맨님의 프로필 이미지
            런닝맨

            Reviews 2

            Average Rating 5.0

            5

            10% enrolled

            이론은 어느정도 알고 있는데, 그동안 구현 부분에서 개인적으로 미진한 부분이 많았는데, 요 강의가 최대한 쉽고, 자세하게 되어 있어서 많은 도움이 되었습니다.

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