The 11 best machine learning courses for 2024

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If you’re keen on data science or artificial intelligence (AI), enrolling in a machine learning (ML) course could be a significant step forward in your information technology career. The need for engineers with ML expertise is rapidly increasing as organisations aim to integrate and prioritise ML in their products.

Given current industry trends, it’s not surprising that a machine learning engineer can command an average salary of $160,099 per year in the US as of 2023, according to Indeed.

If you want to get into machine learning, there are plenty of online materials that cater for a variety of experiences and skillsets. The below list of the best machine learning courses contains learning materials that we feel are a great place to start, but it offers just a snapshot of what's out there.

Disclaimer

Our list of the best machine learning courses has been populated based on a combination of factors, including community reviews, the convenience of each course, and whether they cater for absolute beginners or a current professional looking to up-skill or retrain. Our goal is to provide a broad range of options to give you a good sense the market today.

The 11 best machine learning courses

There are numerous machine learning courses available online. Here are 11 of our favorites.

1. Supervised Machine Learning: Regression and Classification

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Provider: DeepLearning.AI (via Coursera)

Price: $49 (£38) per month

Course link: Supervised Machine Learning: Regression and Classification

Course length: 15 hours

This course, taught by Andrew Ng, provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include supervised learning, unsupervised learning, best practices in machine learning, and case studies and applications.

The course is part of DeepLearning.AI's Machine Learning Specialization, which allows you to further your studies if you wish – although you will need to purchase these separately.

Andrew Ng is widely regarded as one of the foremost experts on machine learning. This Coursera course also has outstanding reviews, and is praised for its structure and case studies that clearly explain ML fundamentals.

2. Machine Learning Foundations: A Case Study Approach

A screenshot of the Coursera website advertising the 'Machine Learning Foundations: A Case Study Approach' course

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Provider: University of Washington (via Coursera)

Price: $79 (£61) per month

Course link: Machine Learning Foundations: A Case Study Approach

Course length: 18 hours

With this comprehensive introduction to machine learning, you’ll gain hands-on experience through practical case studies, learning to predict house prices, analyse sentiment from reviews, retrieve documents, recommend products, and search for images. The course focuses on understanding tasks, matching them to machine learning tools, and assessing output quality. You’ll learn to identify applications of machine learning, apply various techniques, represent data as features, assess model quality, and build applications with machine learning at their core.

The course is also part of a larger specialization offered by the University of Washington, again allowing you to purchase additional individual courses to develop your skills further.

This highly-rated Coursera course is praised for its practical approach using real-world case studies. Reviewers found the hands-on format greatly improved their ML skills.

3. Machine Learning for All

A screenshot of the Coursera website advertising the 'Machine Learning for All' course

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Provider: University of London (via Coursera)

Price: $59 (£46) - included with Coursera Plus

Course link: Machine Learning for All

Course length: 21 hours

This machine learning course, hosted by the University of London's Prof Marco Gillies, is designed to introduce the fundamentals to those with little to no programming knowledge. It looks at machine learning basics and offers a hands-on approach.

The course includes the opportunity to complete a machine learning project, such as training a computer to recognise images, with the help of user-friendly tools developed by Goldsmiths, the University of London.

This intro course gets rave reviews for making ML accessible for beginners with no programming experience.

4. Machine Learning with Python

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Provider: IBM (via Coursera)

Price: $38 (£29)

Course link: Machine Learning with Python

Course length: 12 hours

This module introduces you to the world of Machine Learning using Python, whether you’re looking to advance your data science career or get started in machine learning and deep learning

It begins with an introduction to machine learning concepts, including supervised and unsupervised learning, regression, and classification techniques. Emphasis is placed on hands-on learning, working with Python libraries like SciPy and scikit-learn to apply your knowledge through labs and a final project. By the end of the course, you’ll have job-ready skills and a certificate in machine learning.

Like other courses on this list, IBM's machine learning course is part of a wider group of specializations offered on Coursera, specifically AI engineering and Data Science Professional - both of which offer certificates upon completion, at an extra cost.

IBM's course is valued for emphasizing practical application of ML using Python. Hands-on labs and projects provide learners with job-ready skills, and we also appreciate how flexible the course is when it comes to potential jobs routes and further specializations.

5. Machine Learning (Georgia Tech)

A screenshot of the Udacity website advertising the 'Machine Learning' course

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Provider: Georgia Tech (via Udacity)

Price: $249 (£194) per month

Course link: Machine Learning

Course length: 16 weeks

This course, CS7641 at Georgia Tech, is part of the Online master’s degree (OMS) program. It covers the area of Artificial Intelligence concerned with computer programs that improve their performance through experience. 

The first part covers Supervised Learning, enabling computers to recognise voice, filter spam, and more. The second part covers Unsupervised Learning, used by companies like Netflix and Amazon to make predictions, while the concluding section teaches Reinforcement Learning algorithms for designing self-learning agents.

As part of Georgia Tech's respected master's program, this course offers proven ML training, with reviewers appreciating the theory combined with practical application.

6. Machine Learning Crash Course with TensorFlow APIs

A screenshot of a Google learning platform advertising a free course on machine learning with TensorFlow concepts

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Provider: Google

Price: Free

Course link: Machine Learning Crash Course with TensorFlow APIs

Course length: 15 hours

This course introduces machine learning using TensorFlow APIs. The system provides a fast-paced, practical introduction to machine learning, featuring video lectures, real-world case studies, and hands-on practice exercises. 

It includes 25 lessons, 30+ exercises, and takes approximately 15 hours to complete. The course covers key machine learning concepts and best practices, with lectures from Google researchers and interactive visualisations.

This course makes the list largely because it's a free opportunity to hear directly from Google experts. It does a great job of covering ML basics with TensorFlow, and the fast-paced lessons and interactive exercises have proven effective among reviewers.

7. Machine Learning A-Z: AI, Python & R + ChatGPT Bonus [2023]

A screenshot of the Udemy website advertising the 'Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]' course

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Provider: Kirill Eremenko et al (via Udemy)

Price: $109 (£60)

Course link: Machine Learning A-Z: AI, Python & R + ChatGPT Bonus

Course length: 43 hours

The course aims to provide students with a strong intuition of many Machine Learning models, enabling them to make accurate predictions, powerful analyses, and robust models. 

The course covers a range of topics, including supervised and unsupervised learning, reinforcement learning, natural language processing, deep learning, and dimensionality reduction. Students will learn how to choose the appropriate ML model for each problem and apply their knowledge to create added value for their business or personal projects.

Why choose this course? With outstanding reviews, this Udemy course is praised for clearly explaining ML concepts and real-world applications. The comprehensive curriculum provides strong ML foundations.

8. Introduction to Machine Learning in Production

Provider: DeepLearning.AI (via Coursera)

Price: $49 (£38) per month

Course link: Introduction to Machine Learning in Production

Course length: 10 hours

This course, provided by Andrew Ng and DeepLearning.AI, covers the critical components of the ML lifecycle and pipeline and teaches students how to identify and solve problems for structured, unstructured, small, and big data. 

The course also covers topics such as human-level performance, concept drift, model baselines, project scoping and design, and ML deployment challenges.

Part of DeepLearning.AI's highly-rated MLOps Specialization certificate, this course is a good option for those looking for more specialist teaching in the ML lifecycle discipline.


9. Python for Data Science and Machine Learning Bootcamp

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Provider: Jose Portilla (via Udemy)

Price: $100 (£70)

Course link: Python for Data Science and Machine Learning Bootcamp

Course length: 25 hours

This course is hosted by Jose Portilla, a renowned Udemy instructor who has a number of highly rated data science courses on the platform. Here he teaches students how to use Python for data science and machine learning. 

It covers a range of topics, including programming with Python, data analysis with Pandas and NumPy, data visualisation with Matplotlib and Seaborn, and machine learning with Scikit-Learn. The course provides hands-on practice through detailed code notebooks and HD video lectures.

This hands-on Udemy bootcamp gets rave reviews for effectively teaching Python coding and ML application.


10. Machine Learning for Musicians and Artists

A screenshot of the Kadenze website advertising the 'Machine Learning for Musicians and Artists' course

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Provider: Goldsmiths, University of London (via Kadenze)

Price: $20 per month

Course link: Machine Learning for Musicians and Artists

Course length: 56 hours

Fundamental machine learning techniques are taught in this course, which can be used to make sense of human gestures, musical audio, and other real-time data. The course focuses on learning about algorithms, software tools, and best practices that can be immediately employed in creating new real-time systems in the arts. 

Topics covered include classification, regression, segmentation, and the "machine learning pipeline". The course also introduces off-the-shelf tools for machine learning and feature extraction techniques for music, dance, gaming, and visual art.

This course is perfect for those interested in the creative arts but that also need something that simplifies complex ML concepts.


11. Intro to Machine Learning with TensorFlow

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Provider: Josh Bernhard et al (via Udacity)

Price: $249 (£194) per month

Course link: Intro to Machine Learning with TensorFlow

Course length: 2 months

This course is a practical introduction to machine learning using TensorFlow, a popular framework for building and deploying ML models. The course covers foundational ML techniques, such as data manipulation, supervised and unsupervised learning, and deep learning. 

Students will learn how to use TensorFlow to implement various ML algorithms and apply them to real-world problems. The course also includes real-world case studies, interactive visualisations, and hands-on practice exercises. 

The course is intended for students with intermediate Python programming knowledge and basic knowledge of probability and statistics.

Udacity's project-based course is praised for building ML skills through practical TensorFlow projects. Reviewers also value the employable skills the curriculum offers.

Rene Millman

Rene Millman is a freelance writer and broadcaster who covers cybersecurity, AI, IoT, and the cloud. He also works as a contributing analyst at GigaOm and has previously worked as an analyst for Gartner covering the infrastructure market. He has made numerous television appearances to give his views and expertise on technology trends and companies that affect and shape our lives. You can follow Rene Millman on Twitter.