AB 테스트 실무자 완벽 가이드
거친코딩
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Basic
AB test, 통계
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.
Reviews from Early Learners
What you will learn!
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:
- Detects sentence patterns containing words and phrases that are commonly found in spam, such as 'credit card', 'free', 'advertisement', and 'loan'.
- Create an algorithm that detects sentence patterns to classify email spam.
- 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?
Kaggle
Pandas
Matplotlib
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! 🐯
Recommended for
these people
Who is this course right for?
People interested in machine learning
Machine Learning Beginner
Anyone who wants to learn Python visualization
People who want to learn data preprocessing and processing
Need to know before starting?
Python
6,757
Learners
102
Reviews
101
Answers
4.8
Rating
3
Courses
🙌 소개
안녕하세요. 거칠지만 정말 유익한 데이터 분석가 "거친코딩" 입니다.
고려대학교 통계학과 (졸업)
고려대학교 대학원 빅데이터융합학과 (재학)
QS 세계대학평가 평가위원
고려대학교 SW 중심대학 인공지능 심화 수료
고려대학교 KUCC(컴퓨터 동아리) 세션장
고려대학교 학과 5회 수석, 1회 전체 수석
빅데이터분석기사 자격증
빅데이터분석 준전문가(adsp) 자격증
저는 현재 "네카 중 한 곳"에서 파이썬 및 시각화툴(Tableau)를 활용하여 데이터 수집, 가공, 분석, 예측, 시각화, 업무 자동화를 하고 있습니다.
⭐️ 멘토링
데이터 분석 직무를 꿈꾸는 학생들을 위한 효율적 공부법
데이터 분석 현업에 있는 주니어 분석가를 위한 상담
현업에서 IT직군이 아니지만, IT 기술을 활용하여 본인 업무에 적용하고 싶은 분
🌈 멘토링 진행 방식
zoom을 통한 비대면 방식 진행
준비물 : 컴퓨터, 카메라, 이어폰
미리 준비한 질문 사항 혹은 현 상황에 따라 멘토링 진행
🐯 마무리 글
모든 일에는 시작이 가장 중요합니다. 뜨거운 열정으로 이루고자 하는 것을 꼭 이뤄냅시다!..
📨 메일문의
rough_coding@naver.com
Curriculum
All
25 lectures ∙ (9hr 0min)
Published:
Last updated:
Reviews
All
45 reviews
4.9
45 reviews
동해물과백두산이마르고닳도록Reviews 503
∙
Average Rating 5.0
- 현주
Reviews 3
∙
Average Rating 5.0
5
8% enrolled거친코딩님과 멘토링하고 너무 만족해서 이 강의도 듣게되었는데 역시 강의력도 좋으시고, 수업 내용도 알차네요! 감사합니다~! 다른 강의도 더 올려주세요!!
- 거친코딩Instructor
아! 멘토링 이후에 강의도 들으셨군요! 10월 말에 개인화 추천시스템 강의로 찾아 뵙겠습니다 :) -거친코딩 드림-
강사님 연금 데이터 관련 질문 입니다 연습 데이터 url : https://drive.google.com/drive/folders/149jcCyJFKKG5MFaPNWnYYqM2EkzgRz2P?usp=sharing 새로운 데이터 폴더 생성(machine_learning_data) 및 파일 업로드 위 위치로 들어가서 보면, "machine_learning_data" 이라는 공유 폴더가 나오기는 하는데, 그 안에는 jpg file, cvs file 들만 있고, 강의과 관련된 file은 찾을 수 없었습니다 혹시 제가 잘못된 위치를 찾고 있다면, 알려 주셨으면 합니다
- Sunkyu Danny Kim (탈퇴)
Reviews 1
∙
Average Rating 5.0
5
32% enrolled파이썬에 대한 기초 문법부터 모델링 그리고 케글에서 바로 케이스 스터디까지 적용시킬 수 있어서 좋았어요! 이 정도의 퀄리티가 무료강의라니.. 시리즈 강의 기대해봅니다 :)
- 거친코딩Instructor
많은 도움 되셨다니 제가 기쁘네요 ! 더 좋은 강의로 찾아뵙겠습니다 -거친코딩 드림-
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