Artificial intelligence (AI) and machine learning (ML) have numerous applications, from self-driving cars, to natural language processing, to computer vision.

In this Best Courses Guide (BCG), I’ve picked the best free online courses, resources and tutorials to learn applied AI and ML. I’ve used Class Central’s catalog of over 200K courses to find the best online courses. Some courses are focused on more advanced learners while others cover the principles and give you an overview of their applications.

Click on the shortcuts for more details:

• Top Picks
• What is Applied AI & ML?
• Why You Should Trust Us
• Courses Overview
• How We Made Our Picks and Tested Them

Here are our top picks

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What is Applied AI & ML?

You may know a bit about AI and machine learning from here and there. Or perhaps, you’ve actually gotten quite good at them. It’s understandable. These fields are very promising. But now, you don’t really know what to do with the knowledge and skills you’ve learned.

(Just in case you actually don’t know these fields yet, AI is the field of computer science that focuses on allowing computers to demonstrate human-like intelligence. Machine learning is one branch of AI that deals with learning from large amounts of data. If you’re new to these fields, have a look at our Artificial Intelligence or Machine Learning guides.)

The best way to cement your understanding of AI and ML is to put your knowledge into practice. And as it happens, there are countless, powerful ways to apply AI and machine learning. And by doing so, you can develop industry-relevant skills through projects that are not only useful, but fascinating.

Why You Should Trust Us

Class Central, a Tripadvisor for online education, has helped 60 million learners find their next course. We’ve been combing through online education for more than a decade to aggregate a catalog of 200,000 online courses and 200,000 reviews written by our users. And we’re online learners ourselves: combined, the Class Central team has completed over 400 online courses, including online degrees.

Courses Overview

• The largest course in this guide has 510K+ enrollments
• All together, the courses account for 1.6M enrollments
• All of the courses are free or free-to-audit, except for two
• 8 courses are advanced or intermediate level, while the rest are beginner-friendly
• The most represented provider is Coursera with 8 courses.

Best Comprehensive Convolutional Neural Networks Course (DeepLearning.AI)

Convolutional Neural Networks by DeepLearning.AI is a free-to-audit course that explores the world of computer vision and its applications. From autonomous driving to face recognition and radiology image analysis, you’ll learn to build and apply convolutional neural networks to various visual tasks.

Prerequisites: Intermediate Python skills, basic understanding of linear algebra and machine learning.

What you’ll learn:

• Master the fundamentals of Convolutional Neural Networks (CNNs) and their architecture
• Implement deep neural models for multi-class image classification
• Explore case studies of effective CNNs (LeNet-5, ResNet, Inception network)
• Tackle object localization and detection using ConvNets and U-nets
• Build a Siamese network for face recognition
• Apply neural style transfer using deep ConvNets to generate art.

Best Intro to Self-Driving Cars for Engineers (University of Toronto)

Introduction to Self-Driving Cars by the University of Toronto dives into the innovative world of autonomous vehicles. In this free-to-audit course, you’ll explore terminology, design considerations, and safety assessments of self-driving cars.

Note: This advanced course requires a background in engineering or robotics with strong foundations in Linear Algebra, Statistics, Calculus, and Physics, plus Python 3 programming skills. You’ll need a somewhat recent 64-bit Windows or Ubuntu machine with 8 GB RAM to run the CARLA simulator.

What you’ll learn:

• Explore the history and recent progress of autonomous vehicles
• Break down the elements of driving, from identifying road signs to predicting actions
• Examine various self-driving car hardware configurations and software stacks
• Understand environmental representation for decision-making in autonomous vehicles
• Study safety considerations in autonomous vehicle design, both software and hardware
• Learn vehicle modeling and control, including localization, route following, and executing complex maneuvers
• Test control designs and understand challenges of driving at vehicle performance limits
• Develop control code for a self-driving car in a race track simulation (CARLA environment) as your final project.

Best Applied Machine Learning Course in Python (University of Michigan)

Applied Machine Learning in Python by the University of Michigan is a free-to-audit, beginner-friendly course that teaches practical machine learning techniques and concepts. You’ll learn to differentiate between supervised and unsupervised learning, select specific features for model training, and implement machine learning analyses in Python.

Prerequisites: Python programming skills, familiarity with numpy, pandas, and matplotlib libraries.

Recommended (not required): Introduction to Machine Learning with Python textbook.

What you’ll learn:

• Understand fundamental machine learning concepts (supervised vs. unsupervised learning)
• Implement k-nearest neighbors algorithm using scikit-learn
• Explore supervised learning methods for classification and regression
• Balance model complexity and generalization
• Evaluate and select models using various performance metrics
• Study advanced supervised learning methods: tree ensembles and neural networks
• Learn to avoid data leakage and biases in model training.

Best Intro to Computer Vision and Applications (Georgia Institute of Technology)

Teaching a computer to see and interpret images is fascinating, but before showing computers what images are, we too must know how images work.

Introduction to Computer Vision by Georgia Institute of Technology offers a comprehensive exploration of image analysis and information extraction. This content-rich course covers fundamental concepts including feature detection, motion tracking, camera calibration, and image stabilization.

Prerequisites:

• Data structures knowledge
• Proficiency in MATLAB and/or Python with NumPy
• Linear algebra and vector calculus
• Experience with signal processing (helpful but not required).

What you’ll learn:

• Explore the relationship between computational models, algorithms, and real images in computer vision
• Master image processing techniques for various computer vision applications
• Understand camera geometry, models, and multiple view relationships
• Learn feature detection and matching across images
• Study light interaction with materials and image formation
• Analyze motion in videos and implement object tracking
• Introduce machine learning concepts for classification and recognition in computer vision.

Best Practical Natural Language Processing Course with Classification and Vector Spaces (DeepLearning.AI)

Let’s say you have 1,000 product reviews written by buyers. Can you build a system to automatically go through all of these product reviews and figure out what percentage are positive and negative?

Natural Language Processing with Classification and Vector Spaces by DeepLearning.AI teaches you to analyze text data effectively. You’ll learn to perform sentiment analysis on tweets, visualize word relationships, and develop an English-to-French translation algorithm.

What you’ll learn:

• Convert text into vectors for machine learning applications
• Implement sentiment analysis using text classification techniques
• Explore vector space models to capture semantic relationships between words
• Understand and apply locality-sensitive hashing for document searching
• Develop a machine translation algorithm (English to French).

Best Course for Real World Application of Machine Learning (DeepLearning.AI)

Structuring Machine Learning Projects by DeepLearning.AI, taught by legendary ML instructor Andrew Ng, offers industry-level insights typically gained only after years of experience. This free-to-audit course equips you with practical strategies to lead successful machine learning projects.

What you’ll learn:

• Develop effective machine learning strategies
• Optimize ML production workflows
• Diagnose and reduce errors in ML systems
• Set attainable goals based on appropriate metrics
• Properly split datasets for training and testing
• Compare ML performance with human-level performance
• Recognize and address bias, variance, and data mismatch issues
• Manage complex ML settings (e.g., mismatched training/test sets)
• Determine when to use multi-task, transfer, and end-to-end deep learning.

Best Applied Machine Learning Course for Non-Technical Professionals (Alberta Machine Intelligence Institute)

If you’re a non-technical professional who wants to see what the hype with machine learning is all about, this course is for you.

Introduction to Applied Machine Learning by Alberta Machine Intelligence Institute offers a non-technical overview of machine learning concepts and their real-world applications. This free-to-audit course focuses on applying ML solutions to business problems rather than building algorithms.

What you’ll learn:

• Clarify key terms: machine learning, AI, data science, and deep learning
• Understand the three types of machine learning: supervised, unsupervised, and reinforcement
• Compare machine learning to human learning
• Transform business needs into machine learning problems
• Explore data acquisition, ethics, and pipeline management
• Master the Machine Learning Process Lifecycle (MLPL)
• Analyze a case study applying MLPL to a real-world project.

Best Fundamentals of Machine Learning for Healthcare (Stanford University)

Machine learning and AI hold the potential to revolutionize healthcare, with applications ranging from automated screening and diagnosis to genomic analysis to robotics and more.

Stanford University’s Fundamentals of Machine Learning for Healthcare bridges the gap between healthcare and machine learning. This course equips professionals with engineering backgrounds in healthcare, health policy, pharmaceutical development, and data science with critical knowledge to evaluate and apply ML technologies in medical contexts.

What you’ll learn:

• Understand machine learning principles and their relevance in healthcare
• Master fundamental ML concepts, including problem definition and model training
• Explore various ML models, from simple regression to complex deep learning
• Learn metrics, evaluation techniques, and best practices specific to clinical ML
• Identify and address common challenges and pitfalls in healthcare ML applications
• Develop strategies for effective collaboration in multi-disciplinary teams
• Examine human factors, societal impacts, and ethical issues in healthcare ML.

Best Machine Learning Course for Musicians and Artists (Goldsmith, University of London)

Machine learning is not all lifeless 1s and 0s, as proven by Machine Learning for Musicians and Artists by University of London.

This course introduces the fundamentals of machine learning and its applications in creative arts. It focuses on human gesture, musical audio, and real-time data processing for artistic expression.

Prerequisites: No prior machine learning or math knowledge required. Basic programming experience recommended but not mandatory.

What you’ll learn:

• Understand essential machine learning concepts and techniques
• Explore different types of machine learning and feature extraction
• Learn about the machine learning pipeline in artistic contexts
• Master the Gesture Variation Follower for real-time human gesture recognition
• Gain hands-on experience with Wekinator, applying ML to audio, video, and sensors
• Develop skills to apply machine learning in various forms of creative expression.

Great Machine Learning Operations – MLOps Fundamentals (Google)

If you’re someone who wants to go quickly from a machine learning prototype to business-ready production, MLOps (Machine Learning Operations) Fundamentals might suit your needs.

MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Specifically in this course, you’ll learn about the tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

What You’ll Learn:

• Understand MLOps principles and their importance in ML system lifecycle
• Gain an operations perspective on the entire ML process
• Master Google Cloud’s AI Platform and its various services
• Transform Jupyter notebooks into production-ready ML pipelines
• Utilize Kubeflow for automating ML processes
• Integrate ML pipelines into continuous integration stacks
• Get hands-on experience with Google Cloud’s AI Platform
• Focus on practical MLOps implementation strategies
• Learn to transition from prototypes to production-ready systems
• Explore integration of ML workflows into DevOps practices.

How We Made Our Picks and Tested Them

I built this article following the now tried-and-tested methodology used in previous BCGs (you can find them all here). It involves a three-step process:

First, @manoel and I (@elham) started building this guide by leveraging Class Central’s database of 200K+ online courses. We took a look at things like ratings, reviews, and course bookmarks to make a preliminary selection.

This data-driven approach helped us sift through and pinpoint some of the best courses available out there. Most good courses don’t go unnoticed. The very best tend to gather a lot of attention and excellent reviews!

That said, ratings and reviews don’t always tell the whole story. Some courses may be outdated or archived. So the next step was to bring our personal knowledge of online education into the mix.

Second, we used our experience as online learners to evaluate each of our initial picks. We bounced ideas off each other and made iterative improvements to the guide until we were both satisfied with the end result.

We both come from computer science backgrounds and are prolific online learners, having completed about 45 MOOCs between us. Additionally, Manoel has an online bachelor’s in computer science, and I am currently completing my foundation in computer science. Hence, AI and ML are topics we’re both familiar with.

Third, during our research, we came across courses that felt well-made but weren’t all that popular. If we adopted a purely data-centric approach, we would have to leave those courses out of the article, if only because they had fewer enrollments and ratings.

Instead, we took a more holistic approach. We spiced up this list by including a wide variety of Applied AI & ML courses to cater to a diverse range of learners.

After going through this process — combining Class Central data, our experience as lifelong learners, and lots of editing — we arrived at our final guide. So far, we’ve spent more than 10 hours building this BCG, and we intend to continue updating it in the future.

Fabio revised the research and the latest version of this article.