Data science that I just tried with R

🙆🏻‍♀ 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. 🙆🏻‍♂

"99.9% livecoding, learning DataScience line by line."

✅ Precautions

This course focuses more on practice than theory .
You should have basic knowledge of R and general knowledge of machine learning .

🗒 Course Introduction

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.

🌈 Just try fitting a machine learning model

• Let's fit linear regression and decision tree using basic data in R.
• We'll talk about how to interpret regression models and split training and validation data.

🌈 Building a Heart Disease Prediction Model (Linear Regression)

• Let's build a heart disease prediction model using logistic regression analysis.
• Learn about variable selection methods and fit stepwise/forward/backward regression, respectively.

🌈 Machine Learning with Movie Review Sentiment Analysis

• We do everything from collecting movie review data to building emotional models.
• Let's build a model using the Decoument Term Matrix, the most basic text preprocessing method.
• Let's apply the ensemble learning technique.
• We will vectorize reviews using Word2vec and fit a machine learning model to them.

🌈 Learn machine learning through Kaggle data analysis

• We talk about how classes deal with imbalanced data.
• RandomOversampling/SMOTE/DBSMOTE, etc. are suitable.
• Depending on the problem, we will think about ways to improve the model's performance and implement them ourselves.

🙋🏻‍♂️ I'm curious!

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.