![[PyTorch] 쉽고 빠르게 배우는 NLP강의 썸네일](https://cdn.inflearn.com/public/courses/325056/course_cover/b66025dd-43f5-4a96-8627-202b9ba9e038/pytorch-nlp-eng.png?f=avif&w=420)
[PyTorch] 쉽고 빠르게 배우는 NLP
코코
기본적인 자연어처리 기법(Natural Language Processing)과 딥러닝을 활용한 다양한 텍스트 task에 대해 다룹니다.
중급이상
딥러닝, 인공신경망, PyTorch
Data science that I just tried with 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
What you will learn!
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. 🙆🏻♂
"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.
Recommended for
these people
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
8,314
Learners
502
Reviews
136
Answers
4.4
Rating
20
Courses
학부에서는 통계학을 전공하고 산업공학(인공지능) 박사를 받고 여전히 공부중인 백수입니다.
수상
ㆍ 제6회 빅콘테스트 게임유저이탈 알고리즘 개발 / 엔씨소프트상(2018)
ㆍ 제5회 빅콘테스트 대출 연체자 예측 알고리즘개발 / 한국정보통신진흥협회장상(2017)
ㆍ 2016 날씨 빅데이터 콘테스트/ 기상산업 진흥원장상(2016)
ㆍ 제4회 빅콘테스트 보험사기 예측 알고리즘 개발 / 본선진출(2016)
ㆍ 제3회 빅콘테스트 야구 경기 예측 알고리즘 개발 / 미래창조과학부 장관상(2015)
* blog : https://bluediary8.tistory.com
주로 연구하는 분야는 데이터 사이언스, 강화학습, 딥러닝 입니다.
크롤링과 텍스트마이닝은 현재는 취미로 하고있습니다 :)
크롤링을 이용해서 인기있는 커뮤니티 글만 수집해서 보여주는 마롱이라는 앱을 개발하였고
전국의 맛집리스트와 블로그를 수집해서 맛집 추천 앱도 만들었었죠 :) (시원하게 말아먹..)
지금은 인공지능을 연구하는 박사과정생입니다.
Curriculum
All
32 lectures ∙ (7hr 23min)
Course Materials:
Lecture resources
Published:
Last updated:
Reviews
All
6 reviews
3.8
6 reviews
이기혁Reviews 5
∙
Average Rating 4.2
- djchoi
Reviews 18
∙
Average Rating 4.7
- 나경태
Reviews 3
∙
Average Rating 5.0
- plsch
Reviews 34
∙
Average Rating 4.9
- Dongyoub Kim
Reviews 1
∙
Average Rating 3.0
$55.00
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