
Building the Basics of R Programming
coco
This course covers the basics of R programming for those who have no knowledge of R programming.
Beginner
R
Theory and practice are different. We will grasp the basic concepts of machine learning and introduce the core concepts and theories of various essential models. Furthermore, by working with diverse datasets, we will share various techniques and know-how that are helpful in practice.
236 learners
Level Basic
Course period Unlimited

Reviews from Early Learners
5.0
eastone0508
The training was enjoyable.
5.0
김동현
I think it will be of great help in my work.
5.0
blueday
Thank you for the lecture.
Basic concepts of machine learning and artificial intelligence
Linear Regression Analysis
Key concepts of machine learning models you should know
Techniques for addressing class imbalance problems
Concepts and theories of cluster analysis
How to properly analyze data
The first step for beginner data scientists!
Do you want to learn the basic core concepts of machine learning and artificial intelligence? In this course, we introduce the essential core concepts, theories, and various practical techniques required to become a data scientist.
Therefore, in this lecture, I have focused on core conceptual explanations rather than mathematical ones so that even beginners can easily understand. Furthermore, I share the problems encountered when handling data in practice, along with various methods and know-how to solve those issues.
Those who want to know the
core concepts and theories
of machine learning models
Those who want to
grow quickly
as a data scientist
Those who want to learn
practical machine learning techniques and know-how
needed in the field
The course is structured so that after completing all the content, you will at least be able to properly analyze data as a data scientist. Furthermore, you will be able to design appropriate experiments for specific data domains, perform feature selection to improve model performance, and conduct modeling.
Q. Do I need a lot of mathematical knowledge to take this course?
Undergraduate-level statistics is required, but it is fine even if you do not have related knowledge.
Q. Do I need to know how to use R?
Yes, the class proceeds on the assumption that you have some proficiency in R or Python. I recommend taking the <R프로그래밍 기초 다지기> class below.
Solidifying R Programming Basics
If you are new to data analysis and R programming? Free Course
Delivering
core know-how
from experience
Live coding
to learn
vividly
Improve your practical skills
with various
data
It doesn't stop at simply lecturing on machine learning theories and conducting basic exercises to fit them to data. Based on the experience gained from participating in 7 big data competitions (reaching the finals 7 times and winning 5 awards) and various projects, I aim to deliver as much know-how as possible for excelling in data analysis.
To show you the actual process of how I perform data analysis, most of the practice sessions will be conducted through live coding. I will show you in detail how to search for and apply information when you encounter something unknown during the coding process, and I will also share the problems faced while handling data along with the methods used to solve them.
We will work with a variety of datasets. To help you gain practical experience, we will cover diverse data including the Boston House Price prediction data (commonly used as an example), simulation data with strong multicollinearity, Korean movie review sentiment (positive/negative) prediction data, Seoul villa Jeonse price prediction data, and Kaggle's Otto dataset.
We will cover what machine learning is and what can be achieved with it. Additionally, we will explain the differences between machine learning and deep learning and provide a brief introduction to various machine learning and deep learning models. Furthermore, we will discuss the phenomenon of overfitting, which commonly occurs in both the machine learning and deep learning fields.
The linear regression model is always the first model you learn when studying machine learning. Although it is a simple and straightforward model, it tends to be underutilized due to perceptions of poor performance. However, linear regression models are widely used in the industry and remain a powerful tool for linear regression problems. We will focus on the most fundamental theories and concepts.
We will cover essential machine learning models that you must know. The lecture focuses on concepts to make them easy to understand, rather than focusing on mathematical details. Models such as Decision Trees and kNN are rarely used as standalone models anymore, but they are widely utilized in other fields or within other models. Therefore, they should never be neglected. You will learn the concepts and applications of various models, and we will also introduce ShapValue, which is gaining significant attention in the field of eXplainable AI.
The class imbalance problem occurs more frequently across various fields than one might think and causes a wide range of issues. A representative example is that the model learns with a bias toward the majority class, leading to a decline in predictive performance. We introduce various techniques (Resampling methods) to address these problems.
Analyzing data does not simply end with reading data and fitting a model. It is essential to go through basic data preprocessing, create key derived variables to predict the Y-value, and conduct appropriate experimental design. We provide experimental designs suited for various situations and the essential knowledge you need to know as a data scientist.
"There is a significant gap between the theory and practice of machine learning. The world contains diverse domains and data, and analyzing data must go beyond simply training a model. It is essential to support the process with appropriate experimental design tailored to the domain, the creation of derived variables to improve model performance, and model selection based on the purpose of the analysis.
In this course, we explain the concepts and core principles of data science and artificial intelligence as simply as possible, and provide various tips and know-how that can be applied in practice. Through this course, I hope you can improve your skills to enhance your practical sense of data analysis."
Who is this course right for?
Those who want to know the core concepts and theories of machine learning models
Those who want to grow quickly as a data scientist
Need to know before starting?
Undergraduate-level Statistics
Basic R Programming
8,455
Learners
515
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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