# 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.

- Learn at your own pace
- Get hands on experience
- Learn like a machine

## Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.## LOVED BY LEARNERS AT THOUSANDS OF COMPANIES

## 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.