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机器学习教程
获取有关 AI 与机器学习的洞见与最佳实践、提升技能、构建数据文化。通过我们的教程，学习如何最大化发挥机器学习模型的价值。
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Feature Selection in R with the Boruta R Package
Tackle feature selection in R: explore the Boruta algorithm, a wrapper built around the Random Forest classification algorithm, and its implementation!
DataCamp Team
2018年3月7日
Ensemble Learning in R with SuperLearner
Boost your machine learning results and discover ensembles in R with the SuperLearner package: learn about the Random Forest algorithm, bagging, and much more!
Daniel Gremmell
2018年2月20日
Active Learning: Curious AI Algorithms
Discover active learning, a case of semi-supervised machine learning: from its definition and its benefits, to applications and modern research into it.
DataCamp Team
2018年2月9日
Lyric Analysis with NLP & Machine Learning with R
Dive into the lyrics of Prince's music with R: use text mining and Exploratory Data Analysis (EDA) to shed insight on The Artist's career.
Debbie Liske
2018年2月2日
Transfer Learning: Leverage Insights from Big Data
In this tutorial, you’ll see what transfer learning is, what some of its applications are and why it is critical skill as a data scientist.
Lars Hulstaert
2018年1月19日
Machine Learning with Kaggle: Feature Engineering
Learn how feature engineering can help you to up your game when building machine learning models in Kaggle: create new columns, transform variables and more!
Hugo Bowne-Anderson
2018年1月10日
Kaggle Tutorial: Your First Machine Learning Model
Learn how to build your first machine learning model, a decision tree classifier, with the Python scikit-learn package, submit it to Kaggle and see how it performs!
Hugo Bowne-Anderson
2018年1月3日
Kaggle Tutorial: EDA & Machine Learning
In this Kaggle tutorial, you'll learn how to approach and build supervised learning models with the help of exploratory data analysis (EDA) on the Titanic data.
Hugo Bowne-Anderson
2017年12月21日
Convolutional Neural Networks in Python with Keras
In this tutorial, you’ll learn how to implement Convolutional Neural Networks (CNNs) in Python with Keras, and how to overcome overfitting with dropout.
Aditya Sharma
2017年12月5日
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
2017年10月19日
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
2017年10月13日
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
2017年8月24日