Machine learning is one of the most interesting areas of computer science to work in. It applies to tons of industries, applications, and projects — which means you can likely find a job opportunity that fits your passions and interests while working in a super cutting-edge field.
But what is machine learning? And how do you learn machine learning as a newbie?
It’s important to understand the key machine learning algorithms/techniques and how to use them in a programming language to find patterns in data and create models that would be impossible/incredibly time consuming/manually intensive for humans to do on their own.
In this post, we’ll go over what exactly machine learning is, how to learn machine learning, and 13 of the best machine learning courses available on the internet right now. Most of them are beginner-friendly, but some are more intermediate or advanced so you can find a course that suits your exact needs.
Disclosure: I’m a proud affiliate for some of the resources mentioned in this article. If you buy a product through my links on this page, I may get a small commission for referring you. Thanks!
What Is Machine Learning?
By definition, machine learning is a branch of artificial intelligence (AI) and a field of computer science that teaches computers how to learn and act from experience (without being explicitly programmed by humans to act that way). Essentially, it’s about getting computers to perform human-like tasks.
Steps involved in the machine learning process:
- Collecting the data
- Cleaning, preparing, and manipulating the data
- Choosing a model
- Training the model
- Testing data
- Improving over time
Some example applications of machine learning include: self-driving cars, image recognition, speech recognition, recommender systems (like when Netflix recommends shows/movies based on your viewing history), online fraud detection, targeted emails, traffic prediction, conversational chatbots, identifying drugs that can be repurposed to fight Covid-19, etc.
⬇️ Here are some compelling reasons why you should learn machine learning:
- You have the potential to earn over $100,000 per year
- Machine learning jobs are predicted to be worth almost $31 billion by 2024
- Machine learning is used in nearly all industries
- More and more companies are adopting machine learning
- Machine learning engineer job openings grew 344% between 2015 to 2018
What kind of jobs use machine learning? Data scientists, machine learning engineers, software engineers, business intelligence developers, and more. All of which earn over $100k per year, on average:
- Data scientists can earn $122,451 per year on average in the United States
- Machine learning engineers can earn $151,389 per year on average in the US
- Software engineers can earn $111,600 per year on average in the US
- Business intelligence developers can earn $100,092 per year on average in the US
As you can see, not only is it financially lucrative to learn machine learning, but it gives you future-proof skills you can use to build a great career in tech.
Best Courses to Learn Machine Learning
We’ve covered the why, so now let’s look at how to learn machine learning! There are a wide range of machine learning courses to take you from beginner to pro. Some of the edX and Coursera machine learning courses also offer machine learning certifications after you complete them successfully.
Please note that pricing listed below may change in the future!
Start coding now
Stop waiting and start learning! Get my 10 tips on teaching yourself how to code.
1. Machine Learning for All – University of London via Coursera
➡️ Provider / platform: Coursera
👨🏫 Instructor: Dr. Marco Gillies, Senior Lecturer in the Computer Department of Goldsmiths, University of London
💰 Price: $49 for a verified certificate or free to audit
📈 Level: Beginner
⏰ Duration/length: 22 hours
⭐ Rating: 4.7 stars out of 2,390 student ratings
Covers the basic idea of machine learning — even if you don’t have any background in math or programming. You’ll get hands on and use tools developed at Goldsmiths, University of London to do your own machine learning project: collecting a dataset, training a model to recognize images, and testing it. Keep in mind that since this course is an intro to machine learning, it doesn’t cover programming-based machine learning tools like Python and TensorFlow.
2. Understanding Machine Learning – Pluralsight
➡️ Provider / platform: Pluralsight
👨🏫 Instructor: David Chappell, M.S. in computer science, consulting clients have included HP, IBM, Microsoft, Stanford University, and Target
💰 Price: $29/month or $299/year with a Pluralsight subscription
📈 Level: Beginner
⏰ Duration/length: 43 minutes
At less than 45 minutes, this machine learning course is a short, clear introduction to the skill. Covers open source programming language R and the machine learning process, including how to train, test, and use a model. By the end of this course, you’ll know enough to go deeper and pursue more advanced machine learning courses.
3. Machine Learning Specialization – University of Washington via Coursera
➡️ Provider / platform: Coursera
👨🏫 Instructors: Emily Fox & Carlos Guestrin (both Amazon Professors of Machine Learning in the Statistics Department at the University of Washington)
💰 Price: $49/month with a Coursera subscription
📈 Level: Intermediate (some related experience required)
⏰ Duration/length: 7 months with a suggested pace of 3 hours/week
⭐ Rating: 4.7 stars out of 23,236 student ratings
As you progress through four hands-on courses in this Coursera machine learning specialization, you’ll be taken through a series of practical ML case studies. The courses teach you how to implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms, and also provide Python programming experience. Case studies include predicting housing prices, analyzing sentiment and loan default prediction, and finding similar documents.
4. Machine Learning – Columbia University via edX
➡️ Provider / platform: edX
👨🏫 Instructor: John W. Paisley, Assistant Professor in the Department of Electrical Engineering at Columbia University
💰 Price: Free or add a verified certificate for $249
📈 Level: Advanced (requires familiarity with calculus, linear algebra, statistics, probability, and coding)
⏰ Duration/length: 12 weeks with a suggested pace of 8–10 hours per week
A more advanced intro to machine learning, this course covers supervised learning techniques for regression and classification and unsupervised learning techniques (e.g., object recommendation and topic modeling). You’ll walk away knowing how to apply ML models and methods to real world applications like identifying trending news topics, building recommendation engines, ranking sports teams, and plotting the path of movie zombies.
5. Machine Learning Basics – Team Treehouse
➡️ Provider / platform: Team Treehouse
👨🏫 Instructor: Nick Pettit, independent game developer
💰 Price: $25/month with a Treehouse subscription
📈 Level: Beginner
⏰ Duration/length: 58 minutes
Get a quick, clear primer on machine learning in less than an hour. By the end, you’ll have experience writing a little bit of Python code to build a classifier that can make intelligent predictions. Topics covered in this short course include supervised versus unsupervised learning, machine learning frameworks, and machine learning using Python and scikit-learn.
6. Machine Learning – Stanford University via Coursera
➡️ Provider / platform: Coursera
👨🏫 Instructor: Andrew Ng, founding lead of the Google Brain team, co-founder of Coursera, and former chief scientist at Baidu
💰 Price: Free or add a verified certificate for $79
📈 Level: Beginner
⏰ Duration/length: 60 hours
⭐ Rating: 4.9 stars out of 156,670 student ratings
This Coursera machine learning course gives a broad introduction to machine learning, datamining, and statistical pattern recognition. Case studies are used to help you learn how to apply machine learning techniques to real-world applications, such as building smart robots, text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and more. Includes quizzes, programming assignments, etc.
7. Machine Learning A-Z™: Hands-On Python & R In Data Science – Udemy
➡️ Provider / platform: Udemy
👨🏫 Instructor: Kirill Eremenko (data scientist) & Hadelin de Ponteves (AI entrepreneur)
💰 Price: $94.99 (Udemy has frequent sales)
📈 Level: Beginner (just requires some high school-level math skills)
⏰ Duration/length: 44 hours
⭐ Rating: 4.5 star rating out of 142,527 student ratings
10-part machine learning course that covers how to create ML algorithms in both Python and R. Taught by two data science experts. Code templates included (which you can download and use on your own projects). Filled with practical exercises that are based on real-life examples. Topics include data preprocessing, clustering, deep learning, natural language processing, and more.
8. Machine Learning – Georgia Tech via Udacity
➡️ Provider / platform: Udacity
👨🏫 Instructor: Michael Littman (currently a professor of computer science at Brown University), Charles Isbell (Dean of Computing at Georgia Tech), Pushkar Kolhe (Ph.D. Student at Georgia Tech)
💰 Price: Free
📈 Level: Intermediate (requires strong familiarity with probability theory, linear algebra, and statistics, some experience in programming, and a familiarity with neural networks)
⏰ Duration/length: 4 months
This graduate-level machine learning course is offered as CS7641 at Georgia Tech, where it is a part of the Online Masters Degree (OMS). You’ll learn and practice supervised, unsupervised, and reinforcement learning approaches.
9. The Complete Machine Learning Course for Everybody – Mammoth Interactive
➡️ Provider / platform: Mammoth Interactive
👨🏫 Instructor: John Bura, lead developer and CEO at Mammoth Interactive
💰 Price: $19/month for a Mammoth Interactive subscription or $500 for the individual course
📈 Level: Beginners
In this course, you’ll advance through 7 levels of machine learning mastery — from different machine learning mechanisms to commonly used algorithms. You’ll receive a machine learning certificate of completion, which you can feature in your portfolio and share on LinkedIn.
10. Data Science and Machine Learning with Python – Hands On!
➡️ Provider / platform: Skillshare
👨🏫 Instructor: Frank Kane, Founder of Sundog Education, ex-Amazon
💰 Price: $168/year or $32/month with a Skillshare subscription
📈 Level: Beginner
⏰ Duration/length: 9 hours
Comprehensive machine learning course with 68 lectures, 2 projects, and hands-on Python code examples. Concepts are introduced in plain English: “You won’t find academic, deeply mathematical coverage of these algorithms in this course—the focus is on practical understanding and application of them.” It starts with a crash course on Python, so no prior experience is necessary. Topics in this course come from an analysis of real requirements in data scientist job listings from major tech employers.
11. Machine Learning for Everyone – DataCamp
➡️ Provider / platform: DataCamp
👨🏫 Instructor: Hadrien Lacroix, Sara Billen, Lis Sulmont
💰 Price: Free
📈 Level: Beginner
⏰ Duration/length: 4 hours
Non-technical course that covers how machine learning works, when you can use it, the difference between AI and machine learning, and more. 12 videos and 37 exercises. Also takes a closer look at two common use-cases for deep learning: computer vision and natural language processing.
Want to master Python?
Then download my list of favorite Python learning resources.
12. Complete Machine Learning and Data Science: Zero to Mastery
➡️ Provider / platform: Zero to Mastery
👨🏫 Instructor: Andrei Neagoie (Senior Software Developer turned Instructor, Founder of ZTM) & Daniel Bourke (self-taught machine learning engineer)
💰 Price: $29/month or $264/year with a Zero to Mastery subscription
📈 Level: Beginner or intermediate
⏰ Duration/length: 42 hours
Comprehensive, project-based machine learning course where you will build many real-world projects to add to your portfolio. There are two tracks:
- Complete beginners start from the beginning and learn Python. Starts with basics like machine learning 101 and Python and goes into advanced topics like Neural Networks, Deep Learning, and Transfer Learning.
- If you already know programming, skip the section that teaches Python.
You can start learning for free with over 1 hour of free lessons. Students should “be able to confidently build their own projects and start interviewing in 3-6 months.”
13. Learn the Basics of Machine Learning – Codecademy
➡️ Provider / platform: Codecademy
💰 Price: $19.99/month with a Codecademy subscription
📈 Level: Intermediate (requires knowledge of Python, including functions, control flow, lists, and loops)
⏰ Duration/length: 20 hours
In this course, you’ll learn the foundational machine learning algorithms. Topics include linear regression, multiple linear regression, k-nearest neighbors algorithm (k-NN), decision trees, and more. Projects you’ll work on include a breast cancer classifier, a model that predicts Titanic survival, and a honeybee production trend predictor. Keep in mind that this course is best suited for intermediate learners.
Go Forth & Learn Machine Learning!
There’s a machine learning course out there for you, whether you’ve never programmed a day in your life or have more experience and are ready to level up.
Once you’ve got a solid understanding of machine learning, you can dig into other topics like data science, data analysis, business intelligence or business analytics, or the specific area of machine learning that interests you (e.g., self-driving cars, robotics, fraud detection, etc).
Want to pursue a machine learning career path? Check out this podcast episode on how to break into the field!