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Deep Learning & Machine Learning

Machine learning starting from Python's basic libraries

For those who are new to machine learning, we will provide a clear understanding of the direction of study and basic concepts.

(4.9) 수강평 49개

강의소개.상단개요.수강생.short

난이도 초급

수강기한 무제한

Machine Learning(ML)
Machine Learning(ML)
Pandas
Pandas
Matplotlib
Matplotlib
Scikit-Learn
Scikit-Learn
Kaggle
Kaggle
Machine Learning(ML)
Machine Learning(ML)
Pandas
Pandas
Matplotlib
Matplotlib
Scikit-Learn
Scikit-Learn
Kaggle
Kaggle

먼저 경험한 수강생들의 후기

먼저 경험한 수강생들의 후기

4.9

5.0

Sona Lim

24% 수강 후 작성

I no longer have any resistance to Google Colab, and I like that the lecture notes are posted on the blog so I can review them easily!

5.0

동해물과백두산이마르고닳도록

12% 수강 후 작성

It's a great lecture.

5.0

현주

8% 수강 후 작성

I was so satisfied with the mentoring with Mr. Geoun Coding that I decided to take this course as well. As expected, his teaching skills are great, and the class content is very informative! Thank you~! Please upload more lectures!!

강의상세_배울수있는것_타이틀

  • Data preprocessing and processing using the Pandas library

  • Data visualization with Matplotlib and Seaborn libraries

  • Machine learning theory and practice using the scikit-learn library

  • Practical training using Kaggle data

With “rough but useful” rough coding,
Building Python Machine Learning from the Ground Up 📖

Want to take your first steps in machine learning ?

What you must know to start machine learning
Basic libraries and
Learn about real-world machine learning models!
#Pandas #Matplotlib #Seaborn

Oh, are you talking about me?

Machine learning is popular these days
I know it's good, but
I'm so lost as to where to start .

I have already learned machine learning
It is being applied, but
I'm not sure if I know this correctly .

Machine Learning: Why is it important?

Machine learning is becoming increasingly important!
Machine learning is the process of programming computers to learn from data using various statistical algorithms.
By the way, do you know why we use machine learning?

For example, let's take the case of creating a filter to handle spam within a service using traditional techniques.
In this case, we would create a spam filter like this:

  1. Detects sentence patterns containing words and phrases that are commonly found in spam, such as 'credit card', 'free', 'advertisement', and 'loan'.
  2. Create an algorithm that detects sentence patterns to classify email spam.
  3. Algorithm testing and evaluation are conducted.

The above approach may seem simple, but as the problem becomes more complex and the number of rules increases, maintenance becomes difficult.
On the other hand, machine learning can significantly improve maintainability and accuracy by automatically learning patterns that occur in spam.

So we
You need to learn machine learning!

Machine learning learning
What if it just felt difficult?

As machine learning technology becomes widely known and receives much love and popularity, there are countless related courses available. However, most of these courses follow a similar pattern, focusing only on the topic or concept in a rigid manner. They lack explanations of how it can be applied and utilized in practice.

So, unlike other lectures, this lecture doesn't go straight into the topic of machine learning.
Instead, we will learn about the libraries that are absolutely necessary before actually doing machine learning, while freely preprocessing and visualizing real data , and then learn about the overall machine learning concepts.

So that you can learn machine learning 'properly'.

💡 You can set the direction to start machine learning.

💡 You can learn the basic concepts of machine learning.

💡 You can develop the capabilities needed for analysis in addition to machine learning.

Based on the know-how we've accumulated over the years, we'll help you learn machine learning effectively.
Would you like to try machine learning together?

Attention, these people!

In Python data analysis
Anyone interested

Studying machine learning
For beginners

Data preprocessing and processing
Those who want to learn

Machine learning theory
Those who want to review

Please check your player knowledge!

  • You should know the basic grammar of the programming language Python .

What do you learn?

캐글

Kaggle

판다스, 팬더스

Pandas

맷플롯립, 매트플롯, 맷플롯, 맷플롯라이브러리, 매트플롯라이브러리

Matplotlib

사이킷런, 싸이킷런

Scikit-Learn

In this lecture
Check out the benefits.

Just the essentials!

Many other products on the market
Unlike machine learning lectures,
Only the essential content
Let me give you a brief introduction.

Level up through practice

It doesn't stop at theory
scikit-learn built-in and
Using Kaggle data
We provide practical training.

Machine Learning for Beginners

Knowing the basics of Python
Tailored to beginners' level
Not difficult
You can learn the concept.

Data analysis too?

Not only machine learning concepts
Required for data analysis
Using the library
We will also introduce it.

So that you can learn machine learning 'properly'.

✅ I will teach you effective study methods based on the know-how I have acquired through learning machine learning so far.

✅ We'll help you recall confusing concepts through theoretical lectures on overall machine learning models.

✅ If you have any questions while studying, please feel free to leave them. I'll try to answer them.

Machine learning built from the ground up,
Let's learn in order!

Week 1: Colab setup and basic hands-on experience with the Pandas library.

  • Data preprocessing using the Pandas library
  • Loading and Saving Data
    • Series
    • DataFrame
    • Selecting and filtering DataFrame rows and columns
    • Delete DataFrame rows and columns
    • Modify DataFrame rows and columns

Week 2: Pandas Library Basics #2

  • Data preprocessing using the Pandas library
    • Review of selecting and filtering DataFrame rows and columns
    • Review of deleting DataFrame rows and columns
    • Review of DataFrame row and column modifications
    • Create a DataFrame group
    • Delete duplicate data
    • Find NaN and change it to another value
    • Using the apply function
    • Extract unique values from a column and check the number
    • Merging two DataFrames

Week 3: Data Visualization with Matplotlib and Seaborn Libraries

  • Understanding and Creating Bar Charts
  • Understanding and Creating Pie Charts
  • Understanding and Creating Line Charts
  • Understanding and Creating Scatter Charts
  • Understanding and Creating Heat Map Charts
  • Understanding and Creating Histogram Charts
  • Understanding and Creating Box Charts

Week 4: Linear Regression Theory and Practice

  • What is linear regression?
  • Training and cost function of linear regression models
  • Optimization methods for linear regression models
    • Batch gradient descent
    • Stochastic gradient descent
    • Mini-batch gradient descent
  • polynomial regression
  • Linear model with regulation
    • Ridge regression
    • Lasso regression
    • ElasticNet
  • Early Stopping

Week 5: Linear Classification Theory and Practice

  • What is logistic regression?
  • Training and cost function of a logistic regression model
  • What is a support vector machine?
  • Classification of support vector machines
    • Hard margin classification
    • Soft Margin Classification

Week 6: Decision Tree Model Theory and Practice

  • What is a decision tree model?
  • Decision Tree Learning and Visualization
  • Predict
  • Class probability estimation
  • CART training algorithm
  • Computational complexity
  • Genie impurity or entropy
  • regulatory parameters
  • return

Week 7: Ensemble Model Theory and Practice

  • What is an ensemble model?
  • Voting-based classifier
  • Bagging and pasting
    • Bagging and pasting in scikit-learn
    • oob rating
  • Random patches and random subspaces
  • Random Forest
    • Extra Tree
    • Feature Importance
  • Boosting
    • Adaboost
    • Gradient Boosting
  • Stacking

Week 8: Introduction to and Analysis of Kaggle Data
Week 9: Kaggle Data Analysis

Please note before taking the class!

  • In this course, we will use Google Colab as the editor.
  • To ensure a balanced understanding of concepts and applications , the course is structured with a 50/50 ratio of theory to practice . Please check the curriculum for detailed information.
  • We provide lecture materials through our blog. You can find them at the following link. (Shortcut)

nice to meet you!
Introducing rough coding.

Check out the VLOG of knowledge sharer Rough Coding now! 🐯

강의소개.콘텐츠.추천문구

학습 대상은 누구일까요?

  • People interested in machine learning

  • Machine Learning Beginner

  • Anyone who wants to learn Python visualization

  • People who want to learn data preprocessing and processing

선수 지식, 필요할까요?

  • Python

강의소개.지공자소개

7,011

수강생

112

수강평

102

답변

4.8

강의 평점

3

강의_other

🙌 Introduction

Hello. I am "Rough Coding," a rough but truly informative data analyst.

  • Korea University, Department of Statistics (Graduated)

  • Korea University Graduate School, Department of Big Data Convergence (Enrolled)

  • QS World University Rankings Evaluation Committee Member

  • Completed the Advanced Artificial Intelligence course at Korea University, a SW-centered university

  • Session Leader of KUCC (Computer Club), Korea University

  • 5-time Department Valedictorian and 1-time Overall Valedictorian at Korea University

  • Big Data Analysis Engineer Certification

  • Advanced Data Analytics Semi-Professional (ADsP) Certification

  • Operating a Big Data analysis and development blog

  • Operating an AI lecture YouTube channel

I am currently working at "one of the NAVER/Kakao companies" performing data collection, processing, analysis, prediction, visualization, and task automation using Python and visualization tools (Tableau).

⭐ Mentoring

  • Efficient study methods for students dreaming of a career in data analysis

  • Mentoring for junior analysts currently working in the field of data analysis

  • Those who are not in an IT role but wish to apply IT technology to their own work.

🌈 Mentoring Process

  • Conducted remotely via Zoom

  • Requirements: Computer, camera, earphones

  • Mentoring will proceed based on pre-prepared questions or your current situation.

🐯 Closing Remarks

  • The beginning is the most important part of everything. Let's make sure to achieve what you want with burning passion!

📨 Email Inquiry

rough_coding@naver.com

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4.9

49개의 수강평

  • Sunkyu Danny Kim (탈퇴)님의 프로필 이미지
    Sunkyu Danny Kim (탈퇴)

    수강평 1

    ∙

    평균 평점 5.0

    5

    32% 수강 후 작성

    It was great that I could apply the basic grammar of Python to modeling and even case studies right in Kaggle! I can't believe this level of quality is available for free lectures.. I'm looking forward to the series of lectures :)

    • 거친코딩
      지식공유자

      I'm glad that it was helpful! I'll come back with a better lecture -Rough Coding Dream-

  • lightsn524486님의 프로필 이미지
    lightsn524486

    수강평 2

    ∙

    평균 평점 5.0

    5

    24% 수강 후 작성

    I no longer have any resistance to Google Colab, and I like that the lecture notes are posted on the blog so I can review them easily!

    • 거친코딩
      지식공유자

      I'm even happier that you're satisfied~!! As you said, all the lecture contents and source code for the lecture are all on the blog, so if you get stuck while studying, please refer to the blog. Thank you! I will always cheer you on as you study hard. Thank you. -Rough Coding Dream-

  • gkstoa06002932님의 프로필 이미지
    gkstoa06002932

    수강평 3

    ∙

    평균 평점 5.0

    5

    8% 수강 후 작성

    I was so satisfied with the mentoring with Mr. Geoun Coding that I decided to take this course as well. As expected, his teaching skills are great, and the class content is very informative! Thank you~! Please upload more lectures!!

    • 거친코딩
      지식공유자

      Ah! You also took a lecture after the mentoring! I will come back at the end of October for a lecture on personalized recommendation systems :) -Rough Coding Dream-

    • hsmcha9559

      Instructor This is a question about pension data Practice data url: https://drive.google.com/drive/folders/149jcCyJFKKG5MFaPNWnYYqM2EkzgRz2P?usp=sharing Create a new data folder (machine_learning_data) and upload files If you go to the above location, you will see a shared folder called "machine_learning_data", but there are only jpg files and cvs files in it, and I could not find any files related to the lecture. If I am looking for the wrong location, please let me know.

  • 0419skdus9056님의 프로필 이미지
    0419skdus9056

    수강평 5

    ∙

    평균 평점 5.0

    5

    100% 수강 후 작성

    I love it so much

    • 거친코딩
      지식공유자

      Thank you :) I will come back with a better lecture. -Rough Coding Dream-

  • abcedfg님의 프로필 이미지
    abcedfg

    수강평 503

    ∙

    평균 평점 5.0

    5

    12% 수강 후 작성

    It's a great lecture.

    • 거친코딩
      지식공유자

      Thank you! I will try my best to help more students :) -Rough Coding Dream-

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This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

Beginner

Beginner

AI, Generative AI, Deep Learning(DL)

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usefulit

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30%

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(1)

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jobgreegi

$34.10

29%

$24.20

Beginner / Machine Learning(ML), Deep Learning(DL), AI, Generative AI, AI Agent

5.0

(15)

Mission
Mission

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

Beginner

Beginner

Machine Learning(ML), Deep Learning(DL), AI

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[7-Day Complete] Pass MS AI-900 Certification in One Go

jobgreegi

$34.10

29%

$24.20

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5.0

(15)

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Mission
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$12.10

27%

$8.80

Beginner / AWS, AI, Machine Learning(ML), Deep Learning(DL), Generative AI

Get a clear overview of exam types and trends at a glance! Sharpen your practical skills with a total of 120 questions. The perfect preparation for the AWS AI Practitioner exam, designed for systematic learning even when studying alone.

Beginner

Beginner

AWS, AI, Machine Learning(ML)

Complete Preparation for the AWS AI Practitioner (AIF-C01) Certification Practice ExamCourse Thumbnail

Complete Preparation for the AWS AI Practitioner (AIF-C01) Certification Practice Exam

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$12.10

27%

$8.80

Beginner / AWS, AI, Machine Learning(ML), Deep Learning(DL), Generative AI

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