[Python] Easily understand and implement machine learning without math

This course provides essential knowledge needed to understand the structure and operating principles of various artificial neural networks and create good models.

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Q&A 💬

Q. Can non-majors also take the course?

Anyone interested in getting started with machine learning can enroll! Furthermore, we've kept the math content to a minimum, keeping in line with the course's objectives.

Q. Is programming knowledge required?

Basic Python concepts are also covered in the course, so it is not required.

Q. Why should I take this course?

The course is structured based on specialized knowledge and diverse project experience, covering the entire machine learning process. This will help you develop a comprehensive understanding of machine learning tasks . Additionally, it will help you write code more efficiently.

Q. Is mathematical knowledge required?

A basic understanding of functions is all you need. Those who wish to develop machine learning models themselves or conduct optimization research will need to study additional mathematics beyond this course.

Q. What program do you use?

All exercises are conducted on Google Colaboratory, which requires no separate installation. A free Google account is required, and failure to use Colaboratory may result in disruption to the exercises.

강의소개.콘텐츠.추천문구

학습 대상은 누구일까요?

Anyone interested in machine learning/data analysis
Those who want to acquire essential knowledge in machine learning/data analysis

선수 지식, 필요할까요?

Passion to do

강의소개.지공자소개

5,135

수강생

404

수강평

261

답변

4.7

강의 평점

강의_other

Hello.

I am Deep Learning Ho-hyung, and I run a YouTube channel related to deep learning and machine learning.

I majored in mathematics and data analysis, and I have completed and am currently working on numerous deep learning projects.

I am Deep Learning Ho-hyung, and I run a YouTube channel focused on deep learning and machine learning. I majored in mathematics and data analysis, and I have completed and am currently working on numerous deep learning projects.

I have knowledge that I can share with you regarding Artificial Intelligence topics such as machine learning, advanced machine learning, deep learning, optimization theory, and reinforcement learning, as well as mathematical subjects including linear algebra, calculus, probability and statistics, analysis, and numerical analysis.

Nice to meet you all! * Related Experience: Current) Numerous SCI(E) papers and international conference presentations Current) Multiple university advisory roles related to AI Former) Senior Researcher at K-Corp - Data Analysis and

Nice to meet you all!

* Relevant Experience Current) Numerous SCI(E) papers and international conference presentations Current) Multiple university advisory roles related to Artificial Intelligence Former) Senior Researcher at K-Corporation - Data Analysis and Simulation: New Product Development

* Related Experience

Current) Numerous SCI(E) papers and presentations at international conferences

Current) Multiple university advisory roles related to Artificial Intelligence

Former Senior Researcher at K-Company - Data Analysis and Simulation: New Product Development, Performance Improvement, and New Technology Application

Author of "Introduction to PyTorch for Deep Learning" (Selected as a 2022 Sejong Book in the Academic Category)

- Data Analysis and Simulation: New product development, performance enhancement, and application of new technologies. Author of "Introduction to PyTorch for Deep Learning" (Selected as a 2022 Sejong Book in the Academic Category).

커리큘럼

전체

34개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

해당 강의에서 제공: [object Object]

섹션 1. Introduction to Machine Learning

4개 ∙ (강의상세_런타임_분)

1. Download class materials
2. What is machine learning?
08:00
3. Why do people prefer supervised learning?
05:55
4. Why are data processing, model performance, and fairness evaluation important?
06:58

섹션 2. Introduction to Python

7개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

5. Types and Libraries
16:29
6. Conditional statements and loops
08:33
7. Function
12:18
8. Module
06:19
9. class
07:58
10. Draw a graph
07:39
11. Create a folder
02:40

섹션 3. Data and Feature Engineering

5개 ∙ (강의상세_런타임_분)

12. How to handle text data?
09:59
13. Is it okay if the values of the features differ greatly in size?
06:49
14. Should I use all the features I have?
04:38
15. Why do we partition data?
05:42
16. Hands-on - Feature Engineering
10:46

섹션 4. Machine Learning Basic Model

5개 ∙ (강의상세_런타임_분)

섹션 5. Model Evaluation

4개 ∙ (강의상세_런타임_분)

섹션 6. Advanced Machine Learning

2개 ∙ (강의상세_런타임_분)

섹션 7. Machine Learning Tips

4개 ∙ (강의상세_런타임_분)

섹션 8. Machine Learning Practice

3개 ∙ (강의상세_런타임_분)

강의 게시일: 08/03/2023

마지막 업데이트일: 05/13/2025

수강평

전체

45개

4.7

45개의 수강평

dohyeon02251693
수강평 1
∙
평균 평점 5.0
12/04/2025
5
100% 수강 후 작성
creed2110960
수강평 1
∙
평균 평점 5.0
12/21/2025
5
100% 수강 후 작성
This was a lecture where I could learn the overall content of modeling. Highly recommended 😊
- dlbro
  지식공유자
  12/24/2025
  Thank you so much for the course review!!
bskim9783
수강평 8
∙
평균 평점 5.0
12/22/2025
5
32% 수강 후 작성
mjh137got
수강평 1
∙
평균 평점 5.0
12/08/2025
5
100% 수강 후 작성
I was able to learn about machine learning in detail!
- dlbro
  지식공유자
  12/09/2025
  Thank you so much for the review!! Keep up the great work with your studies!
cgkwon
수강평 12
∙
평균 평점 4.0
12/11/2025
4
100% 수강 후 작성

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