머신 러닝 튜토리얼

AI와 머신 러닝에 대한 인사이트와 모범 사례를 확인하고, 역량을 강화하며, 데이터 문화를 구축하세요. 튜토리얼로 머신 러닝 모델을 최대한 활용하는 방법을 배우세요.

기타 주제:

2명 이상을 교육하시나요?DataCamp for Business 사용해 보세요

LDA2vec: Word Embeddings in Topic Models

Learn more about LDA2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors.

Lars Hulstaert

2017년 10월 19일

Web Scraping & NLP in Python

Learn to scrape novels from the web and plot word frequency distributions; You will gain experience with Python packages requests, BeautifulSoup and nltk.

Hugo Bowne-Anderson

2017년 10월 13일

Detecting Fake News with Scikit-Learn

This scikit-learn tutorial will walk you through building a fake news classifier with the help of Bayesian models.

Katharine Jarmul

2017년 8월 24일

Apache Spark Tutorial: ML with PySpark

Apache Spark tutorial introduces you to big data processing, analysis and ML with PySpark.

Karlijn Willems

2017년 7월 28일

Scikit-Learn Tutorial: Baseball Analytics Pt 2

A Scikit-Learn tutorial to using logistic regression and random forest models to predict which baseball players will be voted into the Hall of Fame

Daniel Poston

2017년 6월 20일

Scikit-Learn Tutorial: Baseball Analytics Pt 1

A scikit-learn tutorial to predicting MLB wins per season by modeling data to KMeans clustering model and linear regression models.

Daniel Poston

2017년 5월 4일

Deep Learning with Jupyter Notebooks in the Cloud

This step-by-step tutorial will show you how to set up and use Jupyter Notebook on Amazon Web Services (AWS) EC2 GPU for deep learning.

Dan Becker

2017년 3월 23일

Preprocessing in Data Science (Part 3): Scaling Synthesized Data

You can preprocess the heck out of your data but the proof is in the pudding: how well does your model then perform?

Hugo Bowne-Anderson

2016년 5월 10일

Preprocessing in Data Science (Part 2): Centering, Scaling and Logistic Regression

Discover whether centering and scaling help your model in a logistic regression setting.

Hugo Bowne-Anderson

2016년 5월 3일

Preprocessing in Data Science (Part 1): Centering, Scaling, and KNN

This article will explain the importance of preprocessing in the machine learning pipeline by examining how centering and scaling can improve model performance.

Hugo Bowne-Anderson

2016년 4월 26일