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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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LOVED BY LEARNERS AT THOUSANDS OF COMPANIES

The Best Courses to Take for Machine Learning

Data Engineering Concept

From Spotify recommendations to smartwatches to self-driving cars, machine learning is embedded in your everyday life. But the industry still has huge growth potential and you can help it expand. 

In just a few weeks time, you could have the fundamental skills and knowledge you need for a lucrative career in machine learning thanks to DataCamp’s online courses—with no experience necessary. At DataCamp, there are no dry, lengthy lectures. Instead, we offer a hands-on, interactive approach. 

Get started with Machine Learning for Everyone. Then discover the tools and technologies you need for this fascinating field in DataCamp’s Machine Learning Fundamentals with Python, or take your R skills to the next level with Machine Learning in the Tidyverse.

Data Engineering Concept

Machine Learning Courses for Beginners

Try machine learning on for size with Machine Learning for Everyone, and then we'll introduce you to machine learning with R and Python, along with valuable tools such as PySpark, Keras, Tidyverse, and scikit-learn.

DataCamp’s online machine learning courses for beginners offer practical and valuable information from day one.

Theory

Machine Learning for Everyone

An introduction to machine learning with no coding involved.

Clock2 hours
Hadrien Lacroix Headshot

Hadrien Lacroix

Curriculum Manager at DataCamp

R

Supervised Learning in R: Classification

In this course you will learn the basics of machine learning for classification.

Clock4 hours
Brett Lantz Headshot

Brett Lantz

Data Scientist at the University of Michigan

R

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.

Clock4 hours
John Mount Headshot

John Mount

Co-founder, Principal Consultant at Win-Vector, LLC

Python

Preprocessing for Machine Learning in Python

In this course you'll learn how to get your cleaned data ready for modeling.

Clock4 hours

DataCamp Content Creator

Course Instructor

R

Unsupervised Learning in R

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Clock4 hours
Hank Roark Headshot

Hank Roark

Senior Data Scientist, Boeing

Python

Supervised Learning with scikit-learn

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Clock4 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

Unsupervised Learning in Python

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Clock4 hours
Benjamin Wilson Headshot

Benjamin Wilson

Director of Research at lateral.io

Theory

Machine Learning for Business

Understand the fundamentals of Machine Learning and how it's applied in the business world.

Clock2 hours
Karolis Urbonas Headshot

Karolis Urbonas

Head of Machine Learning and Science

R

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.

Clock4 hours
Dmitriy Gorenshteyn Headshot

Dmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

Python

Machine Learning for Time Series Data in Python

This course focuses on feature engineering and machine learning for time series data.

Clock4 hours
Chris Holdgraf Headshot

Chris Holdgraf

Fellow at the Berkeley Institute for Data Science

Machine Learning Courses with Python

Python is one of the most accessible, versatile, and intuitive computer languages, but don't let that fool you. Python is also a powerhouse for programming, data science, and machine learning. 

On its own, Python is one of the top-earning job skills. However, when you combine Python skills with machine learning, you'll be at the epicenter of some of the world's most exciting technology.

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.

Clock4 hours
Katharine Jarmul Headshot

Katharine Jarmul

Founder, kjamistan

Python

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.

Clock5 hours
Elie Kawerk Headshot

Elie Kawerk

Data Scientist at Mirum Agency

Python

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.

Clock4 hours
Sergey Fogelson Headshot

Sergey Fogelson

VP of Analytics and Measurement Sciences, Viacom

Python

Preprocessing for Machine Learning in Python

In this course, you'll learn how to get your cleaned data ready for modeling.

Clock4 hours

DataCamp Content Creator

Course Instructor

Python

Supervised Learning with scikit-learn

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Clock4 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

Unsupervised Learning in Python

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Clock4 hours
Benjamin Wilson Headshot

Benjamin Wilson

Director of Research at lateral.io

Python

Machine Learning for Time Series Data in Python

This course focuses on feature engineering and machine learning for time series data.

Clock4 hours
Chris Holdgraf Headshot

Chris Holdgraf

Fellow at the Berkeley Institute for Data Science

Python

Linear Classifiers in Python

In this course, you will learn the details of linear classifiers like logistic regression and SVM.

Clock4 hours
Mike Gelbart Headshot

Mike Gelbart

Instructor, the University of British Columbia

Python

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.

Clock4 hours
Violeta Misheva Headshot

Violeta Misheva

Data Scientist

Python

Model Validation in Python

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

Clock4 hours
Kasey Jones Headshot

Kasey Jones

Research Data Scientist

Machine Learning Courses with R

R is an open-source programming language used in statistical modeling and graphics. Because R is open-source, the number of free tools and packages is almost endless. Which is why R is a favorite among machine learning experts.

DataCamp's machine learning program will teach you to use R to model complex relationships, business analytics and strategize in a fun, interactive format.

R

Supervised Learning in R: Classification

In this course, you will learn the basics of machine learning for classification.

Clock4 hours

Brett Lantz

Data Scientist at the University of Michigan

R

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.

Clock4 hours
John Mount Headshot

John Mount

Co-founder, Principal Consultant at Win-Vector, LLC

R

Machine Learning with caret in R

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Clock4 hours
Zachary Deane-Mayer Headshot

Zachary Deane-Mayer

VP, Data Science at DataRobot

R

Unsupervised Learning in R

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Clock4 hours
Hank Roark Headshot

Hank Roark

Senior Data Scientist, Boeing

R

Modeling with tidymodels in R

Learn to streamline your machine learning workflows with tidymodels.

Clock4 hours
David Svancer Headshot

David Svancer

Data Scientist

R

Machine Learning in the Tidyverse

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

Clock5 hours
Dmitriy Gorenshteyn Headshot

Dmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

R

Sentiment Analysis in R

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Clock4 hours
Ted Kwartler Headshot

Ted Kwartler

Adjunct Professor, Harvard University

R

Support Vector Machines in R

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Clock4 hours
Kailash Awati Headshot

Kailash Awati

Senior Lecturer at University of Technology Sydney.

R

Fraud Detection in R

Learn to detect fraud with analytics in R.

Clock4 hours
Bart Baesens Headshot

Bart Baesens

Professor in Analytics and Data Science at KU Leuven

R

Hyperparameter Tuning in R

Learn how to tune your model's hyperparameters to get the best predictive results.

Clock4 hours
Shirin Elsinghorst Headshot

Shirin Elsinghorst

Data Scientist @ codecentric

Popular Machine Learning Courses

Overview key concepts in Machine Learning for Everyone, a code-free introduction to the fundamentals, and go deeper with an Introduction to Deep Learning in Python.

Discover how AI and machine learning technologies can guide businesses into the future with Machine Learning for Business. Or make data and statistical models work harder for you with Machine Learning for Marketing Analytics in R.

Python

Supervised Learning with scikit-learn

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Clock4 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

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!

Clock4 hours
George Boorman Headshot

George Boorman

Core Curriculum Manager, DataCamp

Python

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.

Clock4 hours
Sergey Fogelson Headshot

Sergey Fogelson

VP of Analytics and Measurement Sciences, Viacom

Python

Introduction to Deep Learning with PyTorch

Learn to create deep learning models with the PyTorch library.

Clock4 hours
Ismail Elezi Headshot

Ismail Elezi

Researcher PHD Student at Ca' Foscari University of Venice

R

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.

Clock4 hours
Dmitriy Gorenshteyn Headshot

Dmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

Theory

Human Resources Analytics: Predicting Employee Churn in Python

In this course you'll learn how to apply machine learning in the HR domain.

Clock4 hours
Hrant Davtyan Headshot

Hrant Davtyan

Assistant Professor of Data Science at the American University of Armenia

Python

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.

Clock4 hours
Kevin Huo Headshot

Kevin Huo

Data Scientist

R

Machine Learning with caret in R

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Clock4 hours
Zachary Deane-Mayer Headshot

Zachary Deane-Mayer

VP, Data Science at DataRobot

Theory

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.

Clock4 hours
Jose Hernandez Headshot

Jose Hernandez

Data Scientist, University of Washington

Python

Image Processing with Keras in Python

Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.

Clock4 hours
Ariel Rokem Headshot

Ariel Rokem

Senior Data Scientist, University of Washington

Practice Machine Learning with Templates, Tutorials, and Cheat Sheets

Learning requires more than taking courses; you need opportunities to test your skills and additional support when you're applying them in the real world. That's why we also offer a range of templates, tutorials, and cheat sheets to expand and continue your journey outside of the course curriculum. Are you stuck on a project? Our tutorials will guide you through.  

Get started faster with DataCamp’s range of templates, which tackle everything from Pokémon to climate change. Feel free to take advantage of these and customize them to suit your needs.

Theory

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 Headshot

Karlijn Willems

NumPy Cheat Sheet: Data Analysis in Python

This Python cheat sheet is a quick reference for NumPy beginners.

Karlijn Willems  Headshot

Karlijn Willems

R

xts Cheat Sheet: Time Series in R

Get started on time series in R with this xts cheat sheet, with code examples.

Karlijn Willems Headshot

Karlijn Willems

Python

Scikit-Learn Cheat Sheet: Python Machine Learning

Karlijn Willems  Headshot

Karlijn Willems

Python

Machine Learning, Pipelines, Deployment and MLOps Tutorial

Learn basic MLOps and end-to-end development and deployment of ML pipelines.

Moez Ali Headshot

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  Headshot

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 Headshot

Karlijn Willems

Automated Machine Learning with Auto-Keras

Learn about automated machine learning and how it can be done with auto-keras.

Sayak Paul  Headshot

Sayak Paul

R

Lyric Analysis: Predictive Analytics using Machine Learning with R

In this tutorial, you'll learn how to use predictive analytics to classify song genres.

Debbie Liske  Headshot

Debbie Liske

Machine Learning FAQs

Is machine learning easy to learn?

DataCamp's beginner machine learning courses are a lot of hands-on fun, and they provide an excellent foundation for machine learning to advance your career or business. Within weeks, you'll be able to create models and generate predictions and insights. You'll also learn foundational knowledge of Python and R and the fundamentals of artificial intelligence.

After that, the learning curve gets a bit steeper. Machine learning careers require a deeper understanding of statistics, math, and software engineering, all of which can be mastered at DataCamp.

What is machine learning used for?

In a nutshell, machine learning is a type of artificial intelligence whose algorithms, as they acquire data, produce analytical models and make predictions with little to no human intervention. 

It's difficult to find an industry that doesn't use machine learning. For example, marketers use machine learning to forecast returns on investments in marketing campaigns. Likewise, purchasing departments use machine learning to predict needed inventory.

Businesses of all kinds use machine learning to predict customer behavior, map supply chains, and forecast revenues. Machine learning is used to predict health outcomes and to improve patient satisfaction. Machine learning helps scientists model climate change scenarios, including possible solutions.

More specifically, machine learning is used in smart devices, search engines, and streaming services (when Netflix suggests a show or movie based on your viewing history, that's machine learning). Facial and fingerprint recognition are machine learning, as are online chat boxes.

Machine learning teaches self-driving cars where to go. In addition, it helps banks detect fraud. But you don't have to be in business to leverage machine learning. For example, DataCamp's foundational machine learning classes empower individuals to forecast their finances and even fitness goals. 

What jobs can you get with machine learning skills?

Machine learning skills are valuable in programming, data science, and other computer engineering disciplines. In addition, machine learning is a must for anyone wanting to work in robotics! 

Not all jobs that require machine learning are in tech, though. For example, linguists use machine learning to track ever-changing languages and dialects. In addition, individual departments, such as marketing, accounting, logistics, and purchasing, to name a few, increasingly need machine learning experts to help them make informed business decisions. Knowing machine learning can give you a step up in nearly any position, as modeling and predicting are critical business needs. 

Are machine learning skills in demand?

Robotics and AI are among the fastest-growing disciplines right now, and each field requires machine learning skills. Businesses, non-profits, and government agencies are beginning to take advantage of predictive models to plan for the future. There's a good chance that your department can use people who can create and read those models, even if you aren't in tech.

However, at DataCamp we're, of course, all about the data. And according to the data compiled by LinkedIn, Machine Learning Engineer is the fourth fastest-growing career

But what about numbers 1-3? The top position is Vaccine Specialist, and without machine learning, we might still be sheltering in place with the COVID pandemic. Number two is Diversity and Inclusion Manager, and machine learning is likely the key to overcoming social and algorithmic biases. 

The third fastest-growing job is Customer Marketing Manager, and machine learning helps them analyze and predict customer behavior. If you continue going down LinkedIn's list, it's not hard to imagine that machine learning skills could be in-demand in any of those positions.

How much math do I need to take a machine learning course?

If you're after a higher-level understanding of machine learning, you don't need much math. If you want to dive deeper and make machine learning your career (as opposed to an added value to your existing career), a foundation in statistics and algebra is helpful. If you don't have a mathematical background, that's okay. We'll teach you everything you need, and our instructors are a lot less scary than your high school calculus teacher.

Do I need to download machine learning software to learn on DataCamp?

You do not need to download anything while learning with DataCamp. All the tools we use are web-based.

What is the difference between a free account and a subscription?

DataCamp offers several free courses, including Data Science for Everyone, Data Engineering for Everyone, and of course, Machine Learning for Everyone. If you're unsure about the courses you want to take next, the first class of each course is free.

To go deeper into any of our courses or access additional content such as projects and assessments, check into our very affordable subscription packages./p>