11 Best Machine Learning Courses to Kickstart Your Career

There’s never been a better time to become a machine learning engineer. In addition to exciting job opportunities in the field of artificial intelligence and the chance to have a meaningful impact on sectors such as security, healthcare, education, finance, and entertainment, machine learning engineers are in high demand. ML engineers experienced 344% job growth from 2015-2018, and the Bureau of Labor Statistics expects the profession to continue experiencing high demand throughout the next decade.

As a highly technical profession, getting started in machine learning engineering takes work. Fortunately, many online courses and bootcamps have been launched in recent years to help newcomers and seasoned programmers learn the core skills needed to make it as ML engineers. Below we’ve curated some of the best paid and free courses on the subject — whether you’re a data scientist or analyst looking to advance your career, a complete newcomer hoping to get into ML engineering, or a professional wanting a career change, there’s a course to help you get started. 

What’s the Best Machine Learning Engineering Course?

The best machine learning engineering course is the one that meets your needs. For example, if you’re a complete beginner who is trying to learn the fundamentals of machine learning and polish your skills in mathematics and statistics, then the best ML engineering course will offer a program that focuses on the basics. But if you’re a seasoned data science professional looking to master machine learning engineering, develop ML algorithms that result in accurate predictions, and work with smart robots, then an advanced program that fills in knowledge gaps would be ideal for you.

The Best Paid Machine Learning Courses

Paid programs are typically more comprehensive and rigorous compared to their free counterparts. Many paid courses also offer student support, career counseling, and, in some cases, mentorship from an industry expert.

Machine Learning Career Track (Springboard)

Springboard’s comprehensive Machine Learning Career Track gets graduates workforce-ready by covering foundational topics such as machine learning algorithms, neural networks, natural language processing, and computer vision, while also offering hands-on experience with deploying ML systems to production.

What We Like

Springboard’s self-paced bootcamp spans the basics of machine learning engineering through to advanced machine learning concepts. Learners develop an understanding of real-world ML engineering applications, work on portfolio-worthy capstone projects, receive one-on-one mentorship from an industry expert, and have access to career coaches and counselors who are invested in the success of every graduate.

Details (Cost, Curriculum, Support, etc.)

Springboard’s Machine Learning Career Track is a six-month commitment of 15-20 hours per week. Tuition is $10,900 for those who pay upfront.

The course is one of the most comprehensive of its kind — students build and deploy large-scale machine learning projects, gain experience with tools such as TensorFlow, develop an understanding of logistic regression and linear regression, and gain experience with database mining. Springboard also offers a job guarantee — if you don’t secure a job in a related field within six months of course completion, Springboard will refund the cost of tuition.

Machine Learning A-Z™: Hands-On Python & R in Data Science (Udemy, Kirill Eremenko)

Udemy’s machine learning course is a massive open online course (MOOC), which means anyone can enroll and learn at their own pace. With 320 lectures spanning topics such as data pre-processing, reinforcement learning, simple linear regression, convolutional neural networks, and hierarchical clustering, the course is intended for both beginners and data analysts looking to fill in skills gaps.

What We Like

The only prerequisite for students is having a high school level knowledge of math, and the MOOC nature of Udemy means it has a low barrier to entry. The course is also available in Spanish, German, Italian, and Portuguese.

Details (Cost, Curriculum, Support, etc.)

Udemy’s course consists of 44.5 hours of video lectures, although students can expect to spend additional time on coding exercises. Students can pay a monthly fee of $29.99 to access Udemy’s courses, or buy the machine learning program as a one-off for $84.99. Like most MOOCs, Udemy offers minimal support to students, with no individual mentorship or career counseling services.

Data Science and Machine Learning Bootcamp With R (Udemy, Jose Portilla)

Udemy’s machine learning bootcamp offers students an introduction to how R can be used in data science and ML engineering, and the different ways it can be handled in SQL files and web scraping.

What We Like

With such a narrow focus on R, this course is suited to anyone who wants to learn the basics of a programming language or who simply has an R knowledge gap that they want to fill.

Details (Cost, Curriculum, Support, etc.)

Udemy’s R course consists of 17.5 hours of on-demand videos. Students can pay a monthly fee of $29.99 to access Udemy’s courses, or buy the machine learning program as a one-off for $84.99. Like most MOOCs, Udemy offers minimal support to students, with no individual mentorship or career counseling services.

AI Applications for Growth (Kellogg Executive Education)

Developed by Northwestern University, this executive program is intended for business leaders who want to understand how machine learning and artificial intelligence can give organizations a competitive edge. Unlike some of the other courses on this list that teach students how to do the job of a machine learning engineer, Kellogg’s offering helps decision makers apply AI to their companies.

What We Like

The Kellogg course approaches machine learning and AI from a business perspective and helps equip company leaders, managers, and mid-career professionals with the knowledge, case studies, and vocabulary to incorporate ML engineering into their organizations.

Details (Cost, Curriculum, Support, etc.)

This program takes two months with a 4-6 hour a week time commitment. Classes are delivered over recorded video lectures, and students can go at their own pace within each weekly module. Classes span topics such as AI and customer experience management, AI in autonomous vehicles, and strategy and platforms. The course costs $2,600.

Machine Learning: From Data to Decisions (MIT)

Similar to Northwestern’s executive course, this machine learning program from MIT is designed for organizational leaders, managers, data scientists, technical leads, and consultants who want to better understand how they can use data and machine learning to gain a competitive edge.

What We Like

The course requires no technical prerequisites. It equips students with an understanding of the building blocks of machine learning, the tools that can be used to make predictions such as neural networks and classification, and how to make critical decisions under uncertainty. The course is also offered in Italian, French, Spanish, and Portuguese.

Details (Cost, Curriculum, Support, etc.)

The program runs for eight weeks and consists of eight lesson modules that span understanding data, making predictions through regression, classification, and neural networks, and decision making foundations. It costs $2,300.

Fundamentals of Google AI for Web Based Machine Learning (Google)

This Google-developed course, taught on the EdX platform, touches on both high-level AI concepts and gives students a taste of building their own ML algorithms. Divided into two parts, the first part of the course covers everything from real-world applications of AI to the relationship between data and ML engineering. The second part covers TensorFlow.js.

What We Like

This self-paced course gives students the opportunity to learn directly from Google AI experts. While it doesn’t go as deep or span as many topics as other ML engineering courses, it offers a strong introductory overview to AI and how it can be applied.

Details (Cost, Curriculum, Support, etc.)

Google AI’s course takes two months to complete with a 3-4 hour a week time commitment. The curriculum is limited compared to other ML engineering courses, but offers a valuable overview of the applications of AI and TensorFlow.js. Like most MOOCs, EdX courses offer minimal support to students, with no individual mentorship or career counseling services. The course costs $398.

Deep Learning Specialization (deeplearning.ai)

Developed by the co-founder of Coursera, Andrew Ng, and offered through the Coursera platform, the Deep Learning Specialization is an ML engineering course that requires students to have experience with Python (heavily used in the data science field), linear algebra, and machine learning concepts.

What We Like

For experienced data professionals who want to study advanced ML engineering concepts, this course cuts to the chase. The program spans artificial neural networks, deep learning, mathematical optimization, transformers, recurrent neural networks, and more.

Details (Cost, Curriculum, Support, etc.)

The program takes around five months to complete with an 8 hour a week commitment. Although the course can be read and viewed for free, it costs $49/month to gain full access to the program. Like most MOOCs, Coursera courses offer minimal support to students, with no individual mentorship or career counseling services.

The Best Free Machine Learning Courses

Free online courses are not usually as comprehensive or well supported as paid courses. That said, they offer a low-commitment approach to dipping your toes into machine learning engineering and often provide valuable lessons on programming and AI basics.

Machine Learning in Python (Springboard)

Springboard’s free Machine Learning in Python course is quick, concise, and designed to give those who already have a background in tech (such as knowledge of a programming language) a rigorous and practical understanding of Python machine learning.

What We Like

This program is best suited for tech professionals who want to know how ML engineering can change the way they work. The course is completely self-paced, features hands-on tutorials on coding using real-world data sets, covers the use of tools such as scikit-learn, and gives students a broad overview of common techniques of the field and how they can be implemented.

Details (Cost, Curriculum, Support, etc.)

Those who have a strong background in tech can expect to complete this free course in around 25 hours, or 2-4 weeks. The curriculum spans supervised learning and unsupervised learning, computational science, building deep neural networks, and using tools such as Pandas and NumPy.

Machine Learning (Stanford, Andrew Ng)

This Coursera program offers an accessible introduction to machine learning basics such as data mining, statistical pattern recognition, and logistic regression. The course is offered as a MOOC, which means students learn independently from pre-recorded lectures and readings.

What We Like

Students praise professor Andrew Ng for his ability to explain the mathematical concepts involved in different areas of machine learning. The course is broad and gives students a taste of the various elements that make up machine learning engineering, such as linear algebra, dimensionality reduction, support vector machines, recommender systems, bias/variance theory, and kernels and neural networks.

Details (Cost, Curriculum, Support, etc.)

The course consists of 61 hours of content and takes around 11 weeks to complete. A week is dedicated to each topic, such as linear regression with one variable, and within each week there are videos, readings, and quizzes.

Machine Learning Crash Course (Google)

Google’s crash course in machine learning is designed for those who already have prerequisite knowledge of Python and are comfortable with linear equations, graphs of functions, calculus, histograms, and statistical means. The self-paced program consists of 25 lessons (videos and exercises) that span machine learning concepts such as regularization, feature crosses, and validation sets.

What We Like

Each lesson within the Machine Learning Crash Course includes a video lecture, an additional reading, and an exercise or quiz to help students check their understanding of the topic — exercises often involve real-world data sets to give students hands-on experience.

Details (Cost, Curriculum, Support, etc.)

The crash course takes around 15 hours to complete, with lectures from Google AI experts and exercises based on real-world case studies. As with most free courses, Google doesn’t offer mentorship or support from instructors.

Free Machine Learning Course (fast.ai)

Fast.ai’s machine learning course was designed to be a companion to the book Deep Learning For Coders With Fastai And PyTorch: AI Applications Without A PhD. Built to be accessible to beginners, the course teaches students to train models in computer vision, tabular data, natural language processing, and random forests and gradient boosting; understand how deep learning models work; and the latest deep learning techniques that matter in practice.

What We Like

The accessible nature of it means that even though the course delves into some fairly complex and technical subjects, newcomers can still find a way in. The course was designed so that anyone with high school level math and around a year of programming experience (preferably Python) can follow along.

Details (Cost, Curriculum, Support, etc.)

Fast.ai’s course contains around 20 hours of lessons that students typically complete over seven weeks. Like most MOOCs, the course has no individualized support or mentorship, but students have access to an online forum where they can ask questions of their peers.

Machine Learning Course Considerations

Whether you’re interested in enrolling in a paid course or getting started in something that’s free, the following things are worth keeping in mind.

Online vs in Person

Many people like taking courses online because it offers them flexibility and the ability to go at their own pace. But some struggle without the accountability of traditional classroom learning and face time with instructors. Before you sign up for a machine learning course, it’s important to understand what kind of student you are and what conditions will help you thrive. Are you someone who needs a classroom environment and face-to-face instruction to help you learn? And if you prefer to take a course online, are you better suited to synchronous classes where you have the accountability of real-time lectures and meetings, or do you perform better when left to set your own pace?

Course Provider Reputation

Not all machine learning courses are made the same. Before enrolling — or paying — for a course, look into its success with students. What information does it publish about its graduate and job placement rate? How many students actually complete the course once they begin? And does it offer assurances such as deferred payment or a job guarantee? The existence of a job guarantee typically signals confidence in the program and its ability to help graduates land a relevant job.

Job Support

It’s one thing to learn all the skills needed to do the work of a machine learning engineer; it’s another to get a foot in the door. Look for courses that will support you in both your academic journey and your post-graduation job search. Does a program include industry mentorship or career counseling? Is interview training or portfolio preparation included? Will the program go above and beyond to help give you a competitive edge? These are all things to consider when choosing a course.

Projects and Portfolio

While most courses promise to teach you the skills needed to become a ML engineer, only some will build in capstone projects and assignments that help you put together a competitive portfolio. When deciding between courses, prioritize the program that will give you hands-on experience with projects that both develop and show off your skills.

Since you’re here…
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