machine-learning
머신러닝 입문자 혹은 스터디를 준비하시는 분들에게 도움이 되고자 만든 repository입니다. (This repository is intented for helping whom are interested in machine learning study)
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Download Latest Version (.zip)- 00-Jupyter-Notebook-튜토리얼-(실습).ipynb
- 01-파이썬-자료구조-(실습).ipynb
- 02-파이썬-리스트-튜플-(실습).ipynb
- 03-파이썬-세트-딕셔너리-(실습).ipynb
- 04-파이썬-문자열-(실습).ipynb
- 05-파이썬-연산-(실습).ipynb
- 06-파이썬-비교-논리-삼항연산자-조건문-(실습).ipynb
- 07-파이썬-반복문-(실습).ipynb
- 08-파이썬-Comprehension-(실습).ipynb
- 09-파이썬-함수-(실습).ipynb
- 10-파이썬-내장함수-(실습).ipynb
- 11-파이썬-패키지-모듈-(실습).ipynb
- README.md
- 00-Jupyter-Notebook-튜토리얼-(해설).ipynb
- 01-파이썬-자료구조-(해설).ipynb
- 02-파이썬-리스트-튜플-(해설).ipynb
- 03-파이썬-세트-딕셔너리-(해설).ipynb
- 04-파이썬-문자열-(해설).ipynb
- 05-파이썬-연산-(해설).ipynb
- 06-파이썬-비교-논리-삼항연산자-조건문-(해설).ipynb
- 07-파이썬-반복문-(해설).ipynb
- 08-파이썬-Comprehension-(해설).ipynb
- 09-파이썬-함수-(해설).ipynb
- 10-파이썬-내장함수-(해설).ipynb
- 11-파이썬-패키지-모듈-(해설).ipynb
- README.md
- README.md
- 01-파이썬-자료구조.ipynb
- 02-파이썬-리스트-튜플.ipynb
- 03-파이썬-세트-딕셔너리.ipynb
- 04-파이썬-문자열.ipynb
- 05-파이썬-연산.ipynb
- 06-파이썬-비교-논리-삼항연산자-조건문.ipynb
- 07-파이썬-반복문.ipynb
- 08-파이썬-Comprehension.ipynb
- 09-파이썬-함수.ipynb
- 10-파이썬-내장함수.ipynb
- 11-파이썬-패키지-모듈.ipynb
- 12-파이썬-클래스-상속.ipynb
- README.md
- ld-datascience-part-01.ipynb
- ld-datascience-part-02.ipynb
- titanic-0.81339-FINAL.ipynb
- titanic-data-analysis-V1.ipynb
- titanic-preprocessing-2.ipynb
- titanic-preprocessing.ipynb
- titanic-randomforest-0.80382.ipynb
- bike_sharing_demand.ipynb
- V1-initial-commit.ipynb
- V2-hyperopt.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V0-Speech representation and data exploration.ipynb
- V1-initial-commit.ipynb
- V2-conv2d-model.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- V1-initial-commit.ipynb
- 01-initial-commit.ipynb
- README.md
- 01-펭귄몸무게-예측-pycaret.ipynb
- 01-teddynote-optuna.ipynb
- README.md
- 00-텐서의-기초.ipynb
- 01-텐서를-생성하는-다양한방법.ipynb
- 02-경사하강법-구현.ipynb
- 03-pytorch로-경사하강법-구현.ipynb
- 04-DataSet과-DataLoader.ipynb
- 05-정형데이터-예측모델생성.ipynb
- 06-정형데이터-예측모델생성-subclassing.ipynb
- 07-torchvision-transform.ipynb
- 08-dnn-fashion-mnist.ipynb
- 09-cnn-rps.ipynb
- 10-transfer-learning-cats-vs-dogs.ipynb
- 11-custom-image-loader.ipynb
- 12-alexnet-implementation.ipynb
- 13-image-transforms.ipynb
- 14-GoogleNet-Inception-Module.ipynb
- 16-torchtext-tutorial.ipynb
- 17-embedding-lstm-text-classification.ipynb
- 23-seq2seq-챗봇.ipynb
- 24-seq2seq-with-attention-chatbot.ipynb
- README.md
- sample_dataset.py
- 01_Simple_Linear_Regression.ipynb
- 02_Multi_Variable_Linear_Regression.ipynb
- 03_Logistic_Regression_With_Mnist_Data.ipynb
- 04_TensorBoard_Example.ipynb
- 05_Convolution_Neural_Network_With_Mnist_Dataset.ipynb
- README.md
- 01-삼성전자-주가.csv
- 02-삼성전자-매매동향.csv
- 02-LSTM-stock-forecasting-with-LSTM-financedatareader.ipynb
- LSTM을_활용한_삼성전자_주가예측.ipynb
- TensorBoard_사용법.ipynb
- cats_vs_dogs-transfer.ipynb
- 01-basic-auto-encoder-MNIST.ipynb
- GradientTape.ipynb
- 01-generate-shakespear.ipynb
- LSTM Layer.ipynb
- 01-LeNet-5.ipynb
- 01-seq2seq-chatbot-no-attention.ipynb
- 02-seq2seq-chatbot-attention.ipynb
- README.md
- 01-pandas-자료구조-(실습).ipynb
- 02-pandas-파일입출력-(실습).ipynb
- 03-pandas-조회-정렬-조건-필터-(실습).ipynb
- 04-pandas-통계-(실습).ipynb
- 05-pandas-복제-결측치-(실습).ipynb
- 06-pandas-전처리-추가-삭제-데이터변환-(실습).ipynb
- 07-pandas-groupby-pivottable-(실습).ipynb
- 08-pandas-concat-merge-(실습).ipynb
- 09-pandas-titanic-실습예제-(실습).ipynb
- README.md
- README.md
- simple-pandas-tutorial-(실습).ipynb
- simple-pandas-tutorial-(해설).ipynb
- 01-10mins-to-pandas.ipynb
- 02-pandas-interpolation.ipynb
- 03-입찰공고-데이터분석.ipynb
- 04-코사인유사도-주식패턴.ipynb
- README.md
- house_price.csv
- 01-DataFrame-시각화-(실습).ipynb
- 02-Visualization-Matplotlib-스타일-(실습).ipynb
- 03-Visualization-Matplotlib-그래프-(실습).ipynb
- 04-Visualization-Seaborn-(실습).ipynb
- 05-Visualization-Seaborn-통계그래프-(실습).ipynb
- README.md
- 01-pandas-visualization-tutorial.ipynb
- 02-matplotlib-tutorial.ipynb
- 03-seaborn-tutorial.ipynb
- 01-최소제곱법-(OrdinaryLeastSquares)-(실습).ipynb
- 02-경사하강법-(Gradient-Descent)-(실습).ipynb
- 03-데이터셋-(Dataset)-다루기-(실습).ipynb
- 04-데이터전처리-(Preprocessing)-(실습).ipynb
- 05-분류-(Classifications)-(실습).ipynb
- 06-회귀-(regression)-(실습).ipynb
- 07-logistic-regression-분류-평가지표-(실습).ipynb
- 08-결정트리-(DecisionTree)-(실습).ipynb
- 09-앙상블-(Ensemble)-(실습).ipynb
- 10-비지도학습-(UnsupervisedLearning)-(실습).ipynb
- README.md
- README.md
- CAM.ipynb
- GRAD_CAM.ipynb
- 01-VanillaGAN-MNIST-Tutorial.ipynb
- 02-DCGAN-MNIST-Tutorial.ipynb
- 02-DCGAN-with-simpson-faces.ipynb
- 03-pix2pix-TF-Tutorial.ipynb
- 04-hjk-style-transfer.ipynb
- code_review.md
- DP.xlsm
- HJK_DDPG_2.ipynb
- HJK_DQN_R1.ipynb
- HJK_PG_AC.ipynb
- HJK_PG_AC_CONTINUE.ipynb
- HJK_PG_REINFORCE.ipynb
- HJK_PG_REINFORCE_CONTINUE.ipynb
- README.md
- README.md
- metadata.json
- model.json
- weights.bin
- index.html
- 01-Google-Image-Downloader.ipynb
- app.py
- dataset-report.html
- keras_model.h5
- labels.txt
- README.md
- 01-AutoKeras-ImageClassifier.ipynb
- 02-AutoKeras-ImageClassifier-Advanced.ipynb
- README.md
- README.md
- 01-Auto-Visualization-Tutorial.ipynb
- dataset-report.html
- README.md
- 00-Intro.ipynb
- 01-Classification.ipynb
- 02-Regression.ipynb
- 03-Association-Rules.ipynb
- 04-Anomaly-Detection.ipynb
- README.md
- README.md
- 01-TimeSeries-Prediction.ipynb
- README.md
- seoul_covid.csv
- README.md
- 01-NLP.ipynb
- README.md
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- LICENSE
- README.md
- requirements.txt
- 01-Text-Classification-Simple.ipynb
- 03-huggingface-bert-kor-text-classification.ipynb
- 01-chatgpt.ipynb
- 02-news-article.ipynb
- fonts-nanum.sh
- mecab-colab.sh
- tfcert.py
- korean_stopword.txt
- boston_house_price.csv
- .all-contributorsrc
- .gitignore
- CONTRIBUTING.md
- README.md
- requirements.txt
# Installation Guide
1. Get the code
git clone https://github.com/teddylee777/machine-learning
Downloads the entire project code from GitHub to your computer.
cd machine-learning
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r 11-AutoML/requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
jupyter notebook
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
If it runs without errors and prints output in the terminal, it worked.
// repository documentation
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