
Building the Basics of R Programming
coco
This course covers the basics of R programming for those who have no knowledge of R programming.
입문
R
This is a lecture that teaches you how to do data science through R. You will learn line by line, from loading data to building models and model performance strategies.

Reviews from Early Learners
5.0
djchoi
It's a useful lecture.
5.0
나경태
Good, good
5.0
plsch
I heard it well.
How to fit a machine learning model with R
How to increase machine learning model performance
🙆🏻♀ This is a lecture that teaches you how to do data science using R.
Learn step-by-step, from data loading to model building and model performance strategies. 🙆🏻♂
This course focuses more on practice than theory .
You should have basic knowledge of R and general knowledge of machine learning .
You've learned R and machine learning, but don't know how to do data analysis?
This course teaches you everything from data entry to machine learning model building and strategies for improving model performance, all while typing code line by line.
Q. How much R do I need to know?
A. You should be able to basically import and preprocess data. The introductory R programming course is mandatory, and the intermediate course is optional.
Q. How much knowledge of machine learning and statistics do I need?
A. You should have basic knowledge of statistics (t.test/anova, etc., at the undergraduate liberal arts level) and theoretical knowledge of machine learning (at the undergraduate major level) to make the course easier to follow.
Who is this course right for?
I have studied statistics and machine learning, but I have no practical experience.
Anyone who wants to fit multiple machine learning models
Need to know before starting?
General knowledge of R
Statistics and Machine Learning Fundamentals
8,388
Learners
509
Reviews
136
Answers
4.4
Rating
20
Courses
I am an unemployed scholar who majored in statistics as an undergraduate, earned a PhD in industrial engineering (artificial intelligence), and is still studying.
Awards ㆍ 6th Big Contest: Game User Churn Algorithm Development / NCSOFT Award (2018) ㆍ 5th Big Contest: Loan Delinquency Prediction Algorithm Development / Korea Association for ICT Promotion
Awards
ㆍ 6th Big Contest Game User Churn Prediction Algorithm Development / NCSOFT Award (2018)
ㆍ 5th Big Contest Loan Defaulter Prediction Algorithm Development / Korea Association for ICT Promotion (KAIT) Award (2017)
ㆍ 2016 Weather Big Data Contest / Korea Institute of Geoscience and Mineral Resources President's Award (2016)
ㆍ 4th Big Contest: Development of Insurance Fraud Prediction Algorithm / Finalist (2016)
ㆍ 3rd Big Contest Baseball Game Prediction Algorithm Development / Minister of Science, ICT and Future Planning Award (2015)
* blog : https://bluediary8.tistory.com
My primary research areas are data science, reinforcement learning, and deep learning.
I am currently doing crawling and text mining as a hobby :)
I developed an app called Marong that uses crawling to collect and display only popular community posts,
I also created a restaurant recommendation app by collecting lists of famous restaurants and blog posts from across the country :) (it failed miserably..)
I am currently a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting blog posts and lists of top-rated restaurants across the country :) (though it failed miserably...) Now, I am a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting lists of famous restaurants and blogs from all over the country :) (It failed miserably...) Now, I am a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting lists of famous restaurants and blogs from all over the country :) (It failed miserably...) Now, I am a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting lists of famous restaurants and blogs from all over the country :) (It failed miserably...) Now, I am a PhD student researching artificial intelligence.
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32 lectures ∙ (7hr 23min)
Course Materials:
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1
I've taken a lot of online lectures, but Colsera Fast Campus... was the worst. The lectures are too stream-of-consciousness, so it's messy and distracting. There are too many instances of code being changed because they're wrong. (I've taken 4 lectures so far, and it's like this..) I don't know much, so I'm just listening, but I keep writing and erasing, writing and erasing... It seems like the content really needs to be processed for lectures. I'm not suggesting that there's a problem with the instructor's skills, but I'm saying that lectures need to have a quality check. I'm evaluating it because it's really hard to listen to.
$55.00
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