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Machine Learning Courses

Machine learning courses cover algorithms and concepts for enabling computers to learn from data and make decisions without explicit programming. Build your skills in NLP, deep learning, MLOps and more.
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Recommended for Machine Learning beginners

Build your Machine Learning skills with interactive courses, curated by real-world experts

course

Understanding Machine Learning

BeginnerSkill Level
2 hours
7.8K
An introduction to machine learning with no coding involved.

track

Machine Learning Fundamentals in Python

16 hours
497
Learn the art of Machine Learning and come away as a boss at prediction, pattern recognition, and the beginnings of Deep and Reinforcement Learning.

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Browse Machine Learning courses and tracks

68 results

course

Supervised Learning with scikit-learn

IntermediateSkill Level
4 hours
6K
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!

course

Unsupervised Learning in Python

IntermediateSkill Level
4 hours
3.6K
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

course

MLOps Concepts

IntermediateSkill Level
2 hours
1.1K
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

course

End-to-End Machine Learning

IntermediateSkill Level
4 hours
625
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

course

Machine Learning for Business

BeginnerSkill Level
2 hours
1.4K
Understand the fundamentals of Machine Learning and how its applied in the business world.

course

Cluster Analysis in Python

IntermediateSkill Level
4 hours
1.2K
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

course

Linear Classifiers in Python

IntermediateSkill Level
4 hours
1.2K
In this course you will learn the details of linear classifiers like logistic regression and SVM.

course

Extreme Gradient Boosting with XGBoost

IntermediateSkill Level
4 hours
748
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

course

Introduction to MLflow

AdvancedSkill Level
4 hours
369
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

course

Dimensionality Reduction in Python

IntermediateSkill Level
4 hours
790
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

course

Unsupervised Learning in R

IntermediateSkill Level
4 hours
807
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

course

Supervised Learning in R: Regression

IntermediateSkill Level
4 hours
741
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

course

Machine Learning for Finance in Python

IntermediateSkill Level
4 hours
437
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

course

Hyperparameter Tuning in Python

IntermediateSkill Level
4 hours
647
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

course

MLOps Deployment and Life Cycling

AdvancedSkill Level
4 hours
429
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

course

Model Validation in Python

IntermediateSkill Level
4 hours
669
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

course

Machine Learning with PySpark

AdvancedSkill Level
4 hours
498
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

course

ARIMA Models in Python

AdvancedSkill Level
4 hours
346
Learn about ARIMA models in Python and become an expert in time series analysis.

course

Ensemble Methods in Python

AdvancedSkill Level
4 hours
488
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

course

Feature Engineering for NLP in Python

AdvancedSkill Level
4 hours
407
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

course

Natural Language Processing with spaCy

IntermediateSkill Level
4 hours
415
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.

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Frequently asked questions

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).

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, business 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?

Yes, machine learning skills are in high demand. According to a report by the World Economic Forum, demand for AI and ML specialists is expected to grow by 40% between 2023 and 2027.

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

If you're looking to develop a high-level understanding of machine learning concepts, 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.

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