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Machine Learning Tutorial
Get insights & best practices into AI & machine learning, upskill, and build data cultures. Learn how to get the most out of machine learning models with our tutorials.
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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
March 7, 2018
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
February 20, 2018
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
February 9, 2018
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
February 2, 2018
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
January 19, 2018
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
January 10, 2018
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
January 3, 2018
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
December 21, 2017
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
December 5, 2017
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
October 19, 2017
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
October 13, 2017
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
August 24, 2017