If you’re considering a career in artificial intelligence, you’re on track for an exciting future! Your timing for beginning or continuing to study machine learning couldn’t be better.

The machine learning market is expected to grow at a compound annual growth rate of 44.1%, from $1.41 billion in 2017 to $8.81 billion in 2022. The broader field of AI is projected to create 2.3 million new jobs by 2020.

According to AI Index, a project by Stanford University, the number of active AI startups increased by 113% from January 2015 to January 2018, and venture capital funding for U.S. AI startups grew by 350% from 2013 to 2017. While the growth of active startups remained a steady 28% year over year, startups in the AI space saw exponential growth.

We put together this list of the best machine learning online courses for you to check out, whether you’re an experienced computer scientist or a beginner. (And you can find information about ML certificates here.)

 

Beginner Machine Learning Online Courses

Machine Learning (Stanford): This highly rated Stanford course is a strong introduction to machine learning. Students praise professor Andrew Ng for his ability to expertly explain the mathematical concepts involved in different areas of machine learning.

Level: beginner | Duration: 10 weeks | Best suited for: students with some coding experience

Python Basics for Data Science (IBM): This course, created by IBM for edX, gives students a beginner-friendly way to learn Python for data science. Included are lab exercises and a framework for creating your own scripts.

Level: beginner | Duration: 1 week | Best suited for: students with some Python experience

Machine Learning with Python: A Practical Application (IBM): If you’re someone who’s just acquired knowledge on the basics of data science, this course is for you. Created by IBM, it teaches you supervised versus unsupervised learning, plus statistical modeling and how it relates to machine learning. You’ll also explore algorithms like classification, regression, clustering, and dimensional reduction, along with popular models such as train/test split, root mean squared error, and random forests.

Level: beginner | Duration: 5 weeks | Best suited for: students with knowledge of data science fundamentals

Machine Learning in Python (Springboard): This free, well-structured, 12-hour learning path offers students an introduction to Python in a flexible way that is full of concise yet rigorous hands-on tutorials. It’s a particularly good choice for anyone new to online machine learning courses.

Level: beginner | Duration: self-paced | Best suited for: tech professional curious about how ML can change the way they work

Data Science: Machine Learning (Harvard): This course from Harvard University will teach you how to build a movie recommendation system while learning the fundamentals of machine learning that power it. Part of the broader data science curriculum that Harvard offers, this course focuses on the fundamentals of R for machine learning.

Level: beginner | Duration: 8 weeks | Best suited for: students with knowledge of data science and the basics of programming

 

Intermediate Machine Learning Online Courses

Fast.ai: Combining both machine and deep learning, this massive course is suited for both experienced coders and beginners. Its philosophy is making machine learning accessible to everyone. Fast.ai aims to make deep learning easier to use and to get more people from all backgrounds involved in the field through its free courses for coders, software libraries, cutting-edge research, and vibrant community.

Level: intermediate to advanced | Duration: self-paced | Best suited for: coders who want to get better at deep learning

Deeplearning.ai: Another take on the deep learning courses, this resource offers five different machine learning tracks. Together, they help you understand the fundamentals of deep learning, apply them on real problems, and start (or continue) building a career in AI. A subset of this course teaches you how to structure machine learning projects.

Level: intermediate to advanced | Duration: self-paced | Best suited for: coders wanting to get better at deep learning

Data Engineering, Big Data, and Machine Learning on Google Cloud Platform (Google): Brought to you by some of the best minds at Google, this course will help you design and build data processing systems on Google Cloud. It will teach you how to build end-to-end data pipelines, how to analyze that data, and how to carry out machine learning on it. This course is for you if you want to learn more about handling structured, unstructured, and streaming data. And of course, you’ll also learn how to use Google Brain’s own TensorFlow.

Level: intermediate to advanced | Duration: 4 weeks | Best suited for: coders wanting to learn Tensorflow and handling big data.

 

Advanced Machine Learning Engineering Courses:

Machine Learning (Georgia Tech): The course covers both supervised learning and unsupervised learning. You will learn how your smartphone is able to recognize your voice and how Netflix predicts what you might want to watch next. It covers a range of topics: regression, classification, neural networks, clustering, reinforcement learning, and game theory.

Level: advanced | Duration: 16 weeks | Best suited for: engineers who want to upgrade their existing skill set

Reinforcement Learning (Microsoft): If reinforcement learning is something you want to get better at, this course is for you. It will teach you the fundamentals of how a machine learning algorithm interacts with its environment to achieve a goal, taught by AI researchers and developers from Microsoft.

Level: advanced | Duration: 6 weeks | Best suited for: engineers wanting to get better at reinforcement learning

Reinforcement Learning (Georgia Tech): Taught by two experts in the reinforcement learning field of research at Georgia Tech, this course will help you participate more deeply in the field’s research. Before taking this course, you should have graduate-level experience with machine learning.

Level: advanced | Duration: 16 weeks | Best suited for: engineers who want to participate in the field of reinforcement learning research

Deep Learning Specialization (Deeplearning.ai): This course is for you if you want to take a serious dive into the world of convolutional networks and work on case studies ranging from healthcare and natural language processing to autonomous driving and music generation. Much like the Google course, you’ll get hands-on experience with TensorFlow and will focus on Python.

Level: advanced | Duration: 12 weeks | Best suited for: engineers wanting to specialize in deep learning techniques

Bioinformatics Specialization (UC San Diego): Are you ready to apply your machine learning and deep learning knowledge to genome sequencing and molecular evolution? This 12-month course developed by the University of California at San Diego doesn’t require you to know programming to complete it. Instead, you will get to use an existing toolkit of software to learn and work on problems in one of the fastest-growing fields in medicine and research: bioinformatics.

Level: advanced | Duration: 12 months (self-paced) | Best suited for: engineers wanting to specialize in genome sequencing research

Natural Language Processing (Microsoft): Created by the AI researchers at Microsoft, the course takes a deep dive into the exciting field of natural language processing. You will learn deep learning models for machine translation, structured models for semantic retrieval (sentiment analysis), and other advanced topics in the field.

Level: advanced | Duration: 6 weeks | Best suited for: engineers wanting to specialize in natural language processing

Machine Learning Engineering Career Track (Springboard): While most graduate programs and other machine learning online courses focus on AI/ML concepts, Springboard’s bootcamp wants students to apply what they’re learning. Of the 400 hours of work it takes to complete this course, 100 hours go toward capstone projects. Learners build and deploy large-scale AI systems—with guidance from an experienced machine learning engineer currently working in the industry. (This also is the first ML program to come with a job guarantee!)

Level: advanced | Duration: 6 months | Best suited for: people with a background in software engineering who want career-focused training

Craving more career-focused guidance? Check out How to Build a Career in AI and Machine Learning, our FREE 35-page guide full of tips and resources that will help you start your career in this cutting-edge field.