There’s plenty of work for people in the ever growing machine learning field, but landing a role like machine learning engineer requires cutting-edge technical knowledge. Hence the assortment of tech companies and universities that offer bootcamps and classes in machine learning and artificial intelligence. These learning options vary widely in prerequisites, length and tuition — which means there’s something for everyone.
We rounded up 19 bootcamps, classes and courses that teach the fundamentals of machine learning.
Best Machine Learning Bootcamps & Classes
- MIT — Professional Certificate Program in Machine Learning & Artificial Intelligence
- UC San Diego and Springboard — Machine Learning Engineering Bootcamp
- Amazon Web Services — Machine Learning Learning Plan
- Harvard University — Data Science: Machine Learning
- Stanford University and DeepLearning.AI — Supervised Machine Learning: Regression and Classification
- Udemy — Machine Learning A-Z: AI, Python & R + ChatGPT
Machine Learning Bootcamps and Programs
Bootcamps and programs are best for learners looking for a well-rounded, traditional classroom experience. These experiences can span from several days to several months, and are often guided directly by an instructor. They also intend to prepare graduates for employment opportunities upon completion.
1. Codesmith — Software Engineering Immersive Programs
Though it isn’t specifically a machine learning course, this 13-week program offered remotely or on-site takes students from zero to machine learning and beyond. Starting with basic computer science principles, the curriculum progresses through front- and back-end development with JavaScript into a machine learning unit. There, students delve into key data science concepts and libraries. Designed to prepare students for mid-level or senior-level engineering roles, the course finishes with an open-source product project.
Cost: $21,800
2. MIT — Professional Certificate Program in Machine Learning & Artificial Intelligence
This program consists of a series of two- and three-day intensive courses, all taught by MIT professors through on-campus and live virtual formats. The interdisciplinary classes touch on math, statistics, computer science and programming. Graduation requires at least 16 total days of study, and two core courses: a two-day foundations course and a three-day advanced course, both focused on how machine learning can parse big datasets and text repositories. Students round out their schedules with electives on topics like computer vision.
Cost: $2,500 for two-day core course; $3,500 for three-day core course; varied prices for elective courses
3. UC San Diego and Springboard — Machine Learning Engineering Bootcamp
This nine-month bootcamp transforms software engineers into machine learning engineers. Starting with prior coding or data science experience, candidates learn the fundamentals of machine learning through a mix of digital materials and one-on-one mentorship through UC San Diego’s professional network. Students gain proficiency in the Python data science stack, study areas like natural language processing and, most importantly, practice production engineering — experience that is particularly valued by hiring managers. The final project, in fact, echoes the day-to-day of a machine learning engineer: To graduate, students must build and deploy a machine learning prototype.
Cost: $9,900 upfront or $2,790 monthly
Machine Learning Classes and Courses
Classes and courses often focus on one area of field expertise and tend to take a shorter amount of time to complete than bootcamps, making them ideal for learners with existing full-time commitments. They’re usually self-paced and don’t require scheduled meeting times with instructors. Plus, many courses stand as a cheaper, or sometimes completely free, learning option in comparison to bootcamps.
4. Amazon Web Services — Machine Learning Learning Plan
Before making more than 32 hours of instruction public, Amazon initially developed this free set of courses to train its employees. Different “learning paths” prepare students for different technical roles, including data scientist and developer. In the end, participants can earn a certificate for their proficiency in crafting and deploying machine learning models in AWS.
Cost: Free (with Amazon account)
5. BrainStation — Machine Learning Course Online
This course provides an overview of basic methodologies, processes and problem-solving methods within the machine learning realm to help professionals become tech experts in their workplaces. While this class offers a remote option, students can also attend in-person sessions at BrainStation’s New York, Miami, Toronto, Vancouver and London campuses. Even for those who choose distance learning, all participants get to enjoy collective work sessions in breakout rooms.
Cost: View course package information for pricing
6. California Institute of Technology — Learning from Data (Introductory Machine Learning course)
This 10-week course on Class Central covers the fundamentals of machine learning in 18 lectures, arranged in a narrative arc. First, the course establishes a definition of learning; then it delves into how that process can be automated. Individual lectures, available on YouTube, cover topics like the bias-variance tradeoff, Kernel methodology and more. Meanwhile, students find homework assignments and their final exam on the CIT course site — this digital course is almost identical to the on-campus incarnation.
Cost: Free
7. DataCamp — Machine Learning with caret in R
This four-hour crash course in machine learning — comprising 24 videos and 88 interactive exercises — has been used as an employee training tool by major tech companies like PayPal and Dell. Led by a data science expert and software engineering expert, the course consists of five R-based modules. The first, which is free, focuses on regression models; later modules focus on data preparation and model selection.
Cost: Free (with DataCamp account)
8. Fast.ai — Practical Deep Learning for Coders
Designed for coders with some Python experience, this course works best with the tools Kaggle Notebooks, Paperspace Gradient and Jupyter Notebook, an open-source coding app. Equipped with these basics, students work independently through 25 free, project-based deep learning lessons. In an early one, participants learn to build image classification models that can distinguish image elements (like what kind of dinosaur is in a photo, for example), among other functions.
Cost: Free
9. Google — Machine Learning Crash Course
This free course consists of video lectures from Google researchers, case studies and more than 30 hands-on exercises with included TensorFlow APIs. It takes 15 hours to complete, though it requires some familiarity with Python to start. The curriculum covers core machine learning concepts, training protocols and use cases, which are surprising and plentiful. For instance: Machine learning helped researchers analyze the political implications behind 18th-century writers’ metaphors.
Cost: Free
10. Google Cloud — Machine Learning on Google Cloud Specialization
In this sequence of five Coursera courses, students learn to develop machine learning models in Google Cloud — a platform with hardware, tools and Tensorflow integration suitable for end-to-end engineering. The intermediate-level curriculum covers various cloud capabilities, like model assessment and feature engineering, with a mix of videos, readings and hands-on labs. Students who plug away at this flexible sequence for six hours a week should complete it in about four months.
Cost: Free (with Coursera account)
11. Harvard University — Data Science: Machine Learning
In this around 32-hour edX course, students learn by doing — specifically, by building a movie recommendation system. Along the way, they learn about training data, popular algorithms, cross-validation and regularization. A Harvard professor of biostatistics leads this introductory course.
Cost: Free (with edX account)
12. IBM — Machine Learning with Python: A Practical Introduction
In this edX course, which takes 20 to 30 hours in total, students analyze real-life machine learning applications and experiment with models of their own. Led by Saeed Aghabozorgi, a senior data scientist at IBM, the course breaks down the differences between supervised and unsupervised machine learning, surveying techniques like the train-test split and assessing models by their root mean squared error.
Cost: Free (with edX account)
13. Kaggle — Intro to Machine Learning
This intro course is the shortest on the list, clocking in at just three hours. Created by data scientist Dan Becker, who has worked with an array of Fortune 500 companies, the course gives a brisk overview of seven key topics in machine learning. They include the under- and over-fitting of models, random forest algorithms and more. Each lesson contains a text tutorial and a Python-based coding exercise. Once students complete the course, they can immediately put their newfound knowledge to practice in Kaggle competitions.
Cost: Free
14. NYC Data Science Academy — Data Science with Python: Machine Learning
During this 20-hour, part-time course — which is taught on campus and live remotely — students learn to make predictions based on complex data sets. That means experimenting with discriminant analysis, support vector machines, libraries like NumPy and scikit-learn plus other popular machine techniques under the watchful eye of professional data scientists. Admission requires familiarity with Python, the course’s language of focus.
Cost: View course information for pricing
15. Stanford University and DeepLearning.AI — Supervised Machine Learning: Regression and Classification
This Coursera course, taught by Coursera co-founder and Google alumnus Andrew Ng, starts simply enough — with a review of linear and logistic regressions, a.k.a. high school math. From there, though, the 33-hour curriculum delves into more esoteric topics, including clustering and neural networks. Ng presents the course material in instructional videos, incorporating real-world case studies so students get a sense of how machine learning algorithms impact daily life. Students also complete supplemental readings and quizzes.
Cost: Free (with Coursera account)
16. Sundog Education — Machine Learning, Data Science and Generative AI with Python
In this Udemy course, Frank Kane — who developed recommendation algorithms at Amazon and IMDb — teaches the fundamentals of machine learning in 20 hours of on-demand video lectures, interspersed with hands-on exercises. Students practice creating tools that automatically classify images, data and sentiments, for instance. The course also requires them to build bots and algorithms, and explore the mechanics of modern generative AI.
Cost: $119.99
17. Udacity — Intro to Machine Learning
This free course teaches students to look at data not just as information, but as fodder for algorithms. The curriculum mixes lectures from machine learning professionals with quizzes, guiding students through Python skills, scikit-learn, approaches to dealing with data sets’ outliers and various types of algorithms — not to mention the art of picking the right one for a given project.
Cost: Free (with Udacity account)
18. Udemy — Machine Learning A-Z: AI, Python & R + ChatGPT
This introductory machine learning course is composed of more than 40 hours of video, readings and hands-on exercises, covering a mix of theoretical concepts and practical applications. The goal is to make ideas like convolutional neural networks and dimensionality reduction ultimately feel like tools rather than gibberish. It’s also a rarity in that it requires no coding expertise. Instead, it comes with Python and R templates that students can modify and reuse in personal projects, and even a primer on ChatGPT.
Cost: $139.99
19. University of Washington — Machine Learning Specialization
This sequence of four Coursera courses begins with applications: what can this mysterious “machine learning” technology do? (Well, recommend products and value real estate, among other things.) Next, students delve into the mechanics behindthese use cases. In the process, they learn to fit models that can classify data, retrieve relevant data and more. Each course blends video tutorials and quizzes.
Cost: Free (with Coursera account)
Frequently Asked Questions
Is machine learning bootcamp worth it?
Machine learning bootcamps can serve as an option for gaining knowledge and educational credentials in the field of machine learning. They may be worth it for those who wish to learn more about machine learning but have existing full-time responsibilities or are unable to access education through other post-secondary programs.
What are the advantages of a machine learning bootcamp?
Advantages of a machine learning bootcamp can include:
- Direct access to industry-specific skills, expert knowledge and resources
- Networking opportunities and career placement assistance
- Less expensive than a four-year degree
- Faster to complete than a four-year degree
- Preparing students for employment opportunities upon completion
What are the disadvantages of a machine learning bootcamp?
Disadvantages of a machine learning bootcamp can include:
- Fast-paced environment and steep learning curves
- Lack of depth and advanced knowledge in topics due to accelerated learning timeline
- Variance in costs depending on the provider and type of courses
- Sometimes requiring full-time commitment for completion
- No guarantee of employment upon completion
