Machine Learning Courses
The global machine learning market is worth more than $21 billion, and it’s set to hit $209 billion by 2029. Become part of this booming and lucrative industry with DataCamp's machine learning courses.
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Machine Learning Courses for Beginners
Understanding Machine Learning
An introduction to machine learning with no coding involved.
Hadrien Lacroix
Curriculum Manager at DataCamp
Supervised Learning in R: Classification
In this course you will learn the basics of machine learning for classification.
Brett Lantz
Data Scientist at the University of Michigan
Supervised Learning in R: Regression
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
John Mount
Co-founder, Principal Consultant at Win-Vector, LLC
Preprocessing for Machine Learning in Python
In this course you'll learn how to get your cleaned data ready for modeling.
DataCamp Content Creator
Course Instructor
Unsupervised Learning in R
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Hank Roark
Senior Data Scientist, Boeing
Supervised Learning with scikit-learn
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Hugo Bowne-Anderson
Data Scientist at DataCamp
Unsupervised Learning in Python
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Benjamin Wilson
Director of Research at lateral.io
Machine Learning for Business
Understand the fundamentals of Machine Learning and how it's applied in the business world.
Karolis Urbonas
Head of Machine Learning and Science
Cluster Analysis in R
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Dmitriy Gorenshteyn
Lead Data Scientist at Memorial Sloan Kettering Cancer Center
Machine Learning for Time Series Data in Python
This course focuses on feature engineering and machine learning for time series data.
Chris Holdgraf
Fellow at the Berkeley Institute for Data Science
Machine Learning Courses with Python
Introduction to Natural Language Processing in Python
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Katharine Jarmul
Founder, kjamistan
Machine Learning with Tree-Based Models in Python
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Elie Kawerk
Data Scientist at Mirum Agency
Extreme Gradient Boosting with XGBoost
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Sergey Fogelson
VP of Analytics and Measurement Sciences, Viacom
Preprocessing for Machine Learning in Python
In this course, you'll learn how to get your cleaned data ready for modeling.
DataCamp Content Creator
Course Instructor
Supervised Learning with scikit-learn
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Hugo Bowne-Anderson
Data Scientist at DataCamp
Unsupervised Learning in Python
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Benjamin Wilson
Director of Research at lateral.io
Machine Learning for Time Series Data in Python
This course focuses on feature engineering and machine learning for time series data.
Chris Holdgraf
Fellow at the Berkeley Institute for Data Science
Linear Classifiers in Python
In this course, you will learn the details of linear classifiers like logistic regression and SVM.
Mike Gelbart
Instructor, the University of British Columbia
Sentiment Analysis in Python
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Violeta Misheva
Data Scientist
Model Validation in Python
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Kasey Jones
Research Data Scientist
Machine Learning Courses with R
Supervised Learning in R: Classification
In this course, you will learn the basics of machine learning for classification.
Brett Lantz
Data Scientist at the University of Michigan
Supervised Learning in R: Regression
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
John Mount
Co-founder, Principal Consultant at Win-Vector, LLC
Machine Learning with caret in R
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Zachary Deane-Mayer
VP, Data Science at DataRobot
Unsupervised Learning in R
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Hank Roark
Senior Data Scientist, Boeing
Modeling with tidymodels in R
Learn to streamline your machine learning workflows with tidymodels.
David Svancer
Data Scientist
Machine Learning in the Tidyverse
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Dmitriy Gorenshteyn
Lead Data Scientist at Memorial Sloan Kettering Cancer Center
Sentiment Analysis in R
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Ted Kwartler
Adjunct Professor, Harvard University
Support Vector Machines in R
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Kailash Awati
Senior Lecturer at University of Technology Sydney.
Fraud Detection in R
Learn to detect fraud with analytics in R.
Bart Baesens
Professor in Analytics and Data Science at KU Leuven
Hyperparameter Tuning in R
Learn how to tune your model's hyperparameters to get the best predictive results.
Shirin Elsinghorst
Data Scientist @ codecentric
Popular Machine Learning Courses
Supervised Learning with scikit-learn
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Hugo Bowne-Anderson
Data Scientist at DataCamp
Machine Learning with scikit-learn
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
George Boorman
Core Curriculum Manager, DataCamp
Extreme Gradient Boosting with XGBoost
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Sergey Fogelson
VP of Analytics and Measurement Sciences, Viacom
Introduction to Deep Learning with PyTorch
Learn to create deep learning models with the PyTorch library.
Ismail Elezi
Researcher PHD Student at Ca' Foscari University of Venice
Cluster Analysis in R
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Dmitriy Gorenshteyn
Lead Data Scientist at Memorial Sloan Kettering Cancer Center
Human Resources Analytics: Predicting Employee Churn in Python
In this course you'll learn how to apply machine learning in the HR domain.
Hrant Davtyan
Assistant Professor of Data Science at the American University of Armenia
Predicting CTR with Machine Learning in Python
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Kevin Huo
Data Scientist
Machine Learning with caret in R
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Zachary Deane-Mayer
VP, Data Science at DataRobot
Feature Engineering in R
Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights about your machine learning models.
Jose Hernandez
Data Scientist, University of Washington
Image Processing with Keras in Python
Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.
Ariel Rokem
Senior Data Scientist, University of Washington
Practice Machine Learning with Templates, Tutorials, and Cheat Sheets
Machine Learning Cheat Sheet
In this cheat sheet, you'll have a guide around the top machine learning algorithms, their advantages and disadvantages, and use-cases.
DatCamp Team
SciPy Cheat Sheet: Linear Algebra in Python
This Python cheat sheet is a handy reference with code samples for doing linear algebra with SciPy and interacting with NumPy.
Karlijn Willems
NumPy Cheat Sheet: Data Analysis in Python
This Python cheat sheet is a quick reference for NumPy beginners.
Karlijn Willems
xts Cheat Sheet: Time Series in R
Get started on time series in R with this xts cheat sheet, with code examples.
Karlijn Willems
Scikit-Learn Cheat Sheet: Python Machine Learning
Karlijn Willems
Machine Learning, Pipelines, Deployment and MLOps Tutorial
Learn basic MLOps and end-to-end development and deployment of ML pipelines.
Moez Ali
Time Series Forecasting Tutorial
A detailed guide to time series forecasting. Learn to use python and supporting frameworks. Learn about the statistical modelling involved.
Moez Ali
Python Machine Learning: Scikit-Learn Tutorial
An easy-to-follow scikit-learn tutorial that will help you get started with Python machine learning.
Karlijn Willems
Automated Machine Learning with Auto-Keras
Learn about automated machine learning and how it can be done with auto-keras.
Sayak Paul
Lyric Analysis: Predictive Analytics using Machine Learning with R
In this tutorial, you'll learn how to use predictive analytics to classify song genres.