Introduction(소개)

Here's list of data science tutorials, that I worked on to help train our team in data science. This also shows you the path that you may follow to learn data science.

As you may see, we got started with Python, jumped into pre-processing and exploratory data analysis, learned a number of machine learning and deep learning techniques and explored a number of problems.

다음은 데이터 과학에 대한 팀 교육을 돕기 위해 작업 한 데이터 과학 자습서 목록입니다. 이것은 또한 데이터 과학을 배우기 위해 따라야 할 길을 보여줍니다.

아시다시피, 우리는 파이썬을 시작했고, 전처리 및 탐색 적 데이터 분석에 뛰어 들었고, 많은 머신 러닝과 딥 러닝 기술을 배우고, 많은 문제를 탐구했습니다.

Note

Don't forget to like, share and upvote any tutorial that you may benefit from. Feel free to leave your thoughts/opinion in comments. This is only a fraction of the stuff, more will come in future.

혜택을 받을 수있는 자습서를 좋아하고 공유하고 찬성하는 것을 잊지 마십시오. 의견이나 의견을 남겨주세요. 이것은 단지 일부일 뿐이며 앞으로 더 많이 올 것입니다.

파이썬 시작하기

https://www.kaggle.com/hassanamin/python-getting-started-training

https://www.kaggle.com/hassanamin/python-session-3

전처리 및 탐색 데이터 분석 탐색

https://www.kaggle.com/hassanamin/exploring-preprocessing-steps

https://www.kaggle.com/hassanamin/exploratory-data-analysis-aka-eda-on-red-wine-data

https://www.kaggle.com/hassanamin/principal-component-analysis-with-code-examples

Machine Learning 기본

https://www.kaggle.com/hassanamin/kmeans-clustering-seed-dataset

자연어 처리(Natural Language Processing)

https://www.kaggle.com/hassanamin/glove-based-text-classification

https://www.kaggle.com/hassanamin/nlp-with-deep-learning-and-embedding-layer

PySpark DataFrames

https://www.kaggle.com/hassanamin/pyspark-dataframes

SpaCy

https://www.kaggle.com/hassanamin/learning-spacy-basics

감성분석(Sentiment Analysis)

https://www.kaggle.com/hassanamin/sentiment-analysis-using-nltk

https://www.kaggle.com/hassanamin/sentiment-analysis-using-classification-approach/

가짜 뉴스 분류(Fake News Classification)

https://www.kaggle.com/hassanamin/fake-news-classifier

https://www.kaggle.com/hassanamin/fake-news-classifier-using-glove

https://www.kaggle.com/hassanamin/fake-news-classifier-with-deep-learning-embedding

컴퓨터 시각 인식(Computer Vision)

https://www.kaggle.com/hassanamin/keras-neural-network-for-digit-recognition

https://www.kaggle.com/hassanamin/exploring-cnn-on-mnist

How to start Pyspark in Kaggle ?

https://www.kaggle.com/hassanamin/pyspark-dataframes

Datasets

# ATIS Airline Travel Information System(ATIS Intent Classification Dataset)

https://www.kaggle.com/hassanamin/atis-airlinetravelinformationsystem

Iris Dataset

https://www.kaggle.com/hassanamin/iris-dataset

https://www.kaggle.com/hassanamin/customer-churn

Reference

https://www.linkedin.com/pulse/learning-data-science-dr-syed-hassan-amin/

Note

Please share, upvote and comment to help me create and share more content for the community.
Thank you all.

커뮤니티의 더 많은 컨텐츠를 작성하고 공유 할 수 있도록 공유, 투표 및 의견을 보내주십시오.
모두 감사합니다.

출처 : https://www.kaggle.com/getting-started/125201

Kaggle 스터디를 위한 커리큘럼입니다.
커리큘럼 참여에 있어 "처음부터 끝까지 3번씩 따라쓰고 이해하는 것"이 중요합니다.
그리고 이 과정을 통해 어떠한 Data인지, Project는 어떤 것인지, 어떤 학습이 되었는지
이해하는 것 또한 중요합니다.

이유한님께서 캐글 스터디 전용 커리큘럼을 정리해주셨다고 해서 사이트 또한 함께 공유합니다.

https://kaggle-kr.tistory.com/32

약간의 변경을 했으니, 커리큘럼을 필사적으로 필사하여 따라가봅시다. 

1. 표 데이터(Tabular data)

1-1. 표를 이용한 이진 분류(Binary classification)

1st level. Titanic: Machine Learning from Disaster

• 1주차 : 타이타닉 튜토리얼 1 - Exploratory data analysis, visualization, machine learning
• 2주차 : EDA To Prediction(DieTanic)
• 3주차 : Titanic Top 4% with ensemble modeling
• 4주차 : Introduction to Ensembling/Stacking in Python

2nd level. Porto Seguro’s Safe Driver Prediction

• 5주차 : Data Preparation & Exploration
• 6주차 : Interactive Porto Insights - A Plot.ly Tutorial
• 7주차 : XGBoost CV (LB .284)
• 8주차 : Porto Seguro Exploratory Analysis and Prediction

3rd level. Home Credit Default Risk

• 9주차 : Introduction: Home Credit Default Risk Competition
• 10주차 : Introduction to Manual Feature Engineering
• 11주차 : Stacking Test-Sklearn, XGBoost, CatBoost, LightGBM
• 12주차 : LightGBM 7th place solution

1-2. 표를 이용한 다중 분류(Multi-class classification)

1st level. Costa Rican Household Poverty Level Prediction

• 13주차 : A Complete Introduction and Walkthrough
• 14주차 : 3250feats->532 feats using shap[LB: 0.436]
• 15주차 : XGBoost

1-3. 표를 이용한 회귀(Regression)

1st level. New York City Taxi Trip Duration

• 16주차 : Dynamics of New York city - Animation
• 17주차 : EDA + Baseline Model
• 18주차 : Beat the benchmark!

2nd level. Zillow Prize: Zillow’s Home Value Prediction (Zestimate)

• 19주차 : Simple Exploration Notebook - Zillow Prize
• 20주차 : Simple XGBoost Starter (~0.0655)
• 21주차 : Zillow EDA On Missing Values & Multicollinearity
• 22주차 : XGBoost, LightGBM, and OLS and NN

2. 이미지 데이터(Image classification)

2-1. 이미지를 이용한 이진 분류(Binary classification)

1st level. Statoil/C-CORE Iceberg Classifier Challenge

• 23주차 : Keras Model for Beginners (0.210 on LB)+EDA+R&D
• 24주차 : Transfer Learning with VGG-16 CNN+AUG LB 0.1712
• 25주차 : Submarineering.EVEN BETTER PUBLIC SCORE until now.
• 26주차 : Keras+TF LB 0.18

2-2. 이미지를 이용한 다중 분류(Multi-class classification)

1st level. TensorFlow Speech Recognition Challenge

• 27주차 : Speech representation and data exploration
• 28주차 : Light-Weight CNN LB 0.74
• 29주차 : WavCeption V1: a 1-D Inception approach (LB 0.76)

3. 자연어 처리(Natural language processing)

1st level. Spooky Author Identification

• 30주차 : Spooky NLP and Topic Modelling tutorial
• 31주차 : Approaching (Almost) Any NLP Problem on Kaggle
• 32주차 : Simple Feature Engg Notebook - Spooky Author

2nd level. Mercari Price Suggestion Challenge

• 33주차 : Mercari Interactive EDA + Topic Modelling
• 34주차 : A simple nn solution with Keras (~0.48611 PL)
• 35주차 : Ridge (LB 0.41943)
• 36주차 : LGB and FM [18th Place - 0.40604]

3rd level. Toxic Comment Classification Challenge

• 37주차 : [For Beginners] Tackling Toxic Using Keras
• 38주차 : Stop the S@#$ - Toxic Comments EDA
• 39주차 : Logistic regression with words and char n-grams
• 40주차 : Classifying multi-label comments (0.9741 lb)

4. 딥러닝을 이용한 객체 분할(Object segmentation)

1st level. 2018 Data Science Bowl

• 41주차 : Teaching notebook for total imaging newbies
• 42주차 : Keras U-Net starter - LB 0.277
• 43주차 : Nuclei Overview to Submission

5. 기타 : 이상 검출(anomaly detection), 시각화(visualization)

1st level. Credit Card Fraud Detection

• 44주차 : In depth skewed data classif. (93% recall acc now)
• 45주차 : Anomaly Detection - Credit Card Fraud Analysis
• 46주차 : Semi-Supervised Anomaly Detection Survey

2nd level. Kaggle Machine Learning & Data Science Survey 2017

• 47주차 : Novice to Grandmaster
• 48주차 : What do Kagglers say about Data Science ?
• 49주차 : PLOTLY TUTORIAL - 1