coursework for machine learning

10 Best Machine Learning Courses to Take in 2022

In this article, I’ve compiled a list of the best machine learning courses available online. I built the ranking by following a well-defined methodology that you can find below.

But if you want to jump straight to the results, here are my picks:

• Machine Learning (Stanford University)
• Machine Learning Foundations: A Case Study Approach (University of Washington)
• Machine Learning for All (University of London)
• Machine Learning with Python (IBM)
• Machine Learning (Georgia Tech)
• Machine Learning Crash Course with TensorFlow APIs (Google)
• Machine Learning A-Z: Hands-On Python & R In Data Science (Udemy)
• Introduction to Machine Learning in Production (DeepLearning.AI)
• Python for Data Science and Machine Learning Bootcamp (Udemy)
• Machine Learning for Musicians and Artists (Goldsmith)

What is Machine Learning?

Machine learning is a subfield of artificial intelligence dedicated to the design of algorithms capable of learning from data. It has numerous applications, including business analytics, health informatics, financial forecasting, and self-driving cars.

In 2022, machine learning skills are widely in-demand. On Microsoft’s career page , 21% of the open developer positions currently mention “machine learning”. On Amazon’s career page , it’s 63%.

According to the Future of Jobs Report published by the World Economic Forum, machine learning is expected to be one of the world’s most in-demand skills through 2025.

Course Ranking Methodology

To create this ranking, I followed a three-step process:

First , I’m a developer at Class Central , the leading search engine for online courses.

So I went through our catalog of over 50K courses to put together a preliminary selection. I did so by taking into account factors like reviews, ratings, enrollments, bookmarks, and more.

So this was a rather objective step: I narrowed down the options by looking at well-defined metrics.

Second , I used my experience as an online learner to evaluate each preliminary pick.

Metrics such as course ratings rarely tell the whole story. I’ve completed many MOOCs , earned an online bachelor’s in computer science , and I’m enrolled in Georgia Tech’s online master’s in computer science ( OMSCS ). This has given me some perspective on what to look for in an online course, which I used to evaluate each of my preliminary picks.

So this was a rather subjective step: I combed through my picks to arrive at a near-final selection.

Third , I expanded this selection to include other valuable resources I’ve come across.

Since there are long-established courses in most topics, more recent courses on the same topic can go unnoticed. But sometimes, these are great. I made sure to include those when possible.

So this is a rather subjective step again: I rounded up my picks with excellent but less well-known courses.

The end result is a unique selection of courses that combines a decade of Class Central data and my own experience as an online learner to try to get the best of both worlds. So far, I’ve spent more than 15 hours building this list, and I’ll continue to update it.

Course Ranking Statistics

Here are some statistics regarding this course ranking:

• Combined, these courses have accrued over 6.6M enrollments.
• The most-represented course provider in the ranking is Coursera, with four courses.
• Combined, these courses have been bookmarked over 118,000 times on Class Central, while the machine learning subject itself has been bookmarked over 195,000 times.
• The most-popular course in the ranking has over 4M enrollments by itself.
• Eight courses are free or free-to-audit, while two are paid.
• Combined, these courses are received over 500 reviews on Class Central.

Without further ado, here are my picks for the best machine learning online courses.

1. Machine Learning (Stanford University)

My first pick for best machine learning online course is the aptly named Machine Learning , offered by Stanford University on Coursera.

This is the seminal machine learning course, and a very special course indeed. Taught by Andrew Ng , it was one of the original courses that kicked-off the popularization of massive open online courses (MOOCs).

Bolstered by the course's success, Andrew Ng went on to cofound Coursera.

What You'll Learn

This course starts by laying down the mathematical foundations of machine learning. It begins with a review of linear algebra and univariate linear regression before moving on to multivariate and logistic regression.

It then jumps from topic to topic each week to cover a wide variety of machine learning techniques and models. These include deep learning, support-vector machines, and principal component analysis.

Finally, it touches on practical aspects such as how to design and leverage large-scale machine learning projects.

By the end of the course, you’ll have a broad understanding of machine learning, its concepts, and its methods. You’ll be able to implement fundamental machine learning algorithms such as back propagation and k-means clustering.

You’ll be equipped to tackle tasks such as multi-class classification and anomaly detection. And you’ll be able to use Octave and Matlab to complete practical projects involving optical character recognition using a wide variety of approaches.

One Thing to Note

This course uses Octave rather than, say, Python. The course will teach you the concepts but not the tools most commonly used nowadays in machine learning. Despite that, it remains in my view the machine learning course of choice, hence its top spot.

But if you’re looking for a course more relevant to the day-to-day of a machine learning practitioner, check the next pick.

How You’ll Learn

The course is broken down into 11 weeks. Each week involves about 6 hours of work. Concepts are taught through a combination of video lectures and readings.

In terms of assessments, each week includes at least one auto-graded quiz. Most include several. And most weeks include a multiple-hours long programming project. There are 8 in total.

• The course launched in October 2011 with over 100K learners , just two months after being announced. It was unprecedented for a course to have this many learners.
• With 4.5M learners, it has grown to become one of the most popular online courses ever, if not the most popular ever.
• Besides being a professor of computer science at Stanford University, Andrew Ng was the former Chief Scientist at Baidu and the cofounder of Google Brain.
• The course has amassed over 68,000 bookmarks on Class Central.
• While the course includes a paid certificate, the entirety of the course material, including all the assignments, can be accessed for free.
• The instant popularity of the course encouraged Andrew Ng to found Coursera and, more recently, DeepLearning.AI.

If you're interested in this course, you can find more information about the course and how to enroll here .

2. Machine Learning Foundations: A Case Study Approach (University of Washington)

My second pick for the best machine learning online course is Machine Learning Foundations: A Case Study Approach , offered by the University of Washington on Coursera.

Many academic machine learning courses like to approach the subject from a rather abstract perspective. They spend a lot of time laying down mathematical foundations and relegating more tangible aspects of the discipline to examples and exercises. This course flips that script on its head.

As its name indicates, the course approaches machine learning through case studies, each with a well-defined context and objective. These case studies help ground machine learning concepts in reality.

Instead of learning how to do regressions just because, you’ll learn how to predict house prices with regressions. That doesn’t mean that the course glosses over the theoretical details. It just approaches the subject more pragmatically.

The course starts by contextualizing machine learning: explaining what machine learning is, going over some of its applications, and making a case for its importance in the future.

The course introduction also takes the time to cover Python fundamentals as well as the rudiments of tools like Jupyter Notebooks.

The course then moves from case study to case study, using each one to illustrate a particular facet of machine learning: you use regressions to predict house prices, you use classification to evaluate sentiments in user reviews, you use clustering for grouping related articles, you use deep learning to identify objects in images, and so on.

If you’re someone that likes to learn through examples, the clear mapping between tasks and concepts in this course might help make the subject more palatable to you.

By the end of this course, you’ll understand fundamental machine learning tasks like regression, classification, and clustering, and you’ll know when to use each technique.

You’ll know how to extract features from data and use this as inputs for your models. You’ll be able to evaluate your model's correctness using well-defined error metrics. And you’ll be able to implement machine learning applications end-to-end in Python.

This course is broken down into 6 weeks. Each week involves about 3 hours of work. The course is taught through a mix of short videos and readings.

Regarding assessments, most weeks include two exercises that should take about an hour to complete.

When the course was released, it used GraphLab, an open-source machine learning tool started by Prof. Carlos Guestrin, one of the course co-instructors. Since then, GraphLab has become Turi, and the course now uses TuriCreate for the exercises.

The videos, however, still use GraphLab, and while both tools are similar, this has caused friction for some learners. So if you take the course, be prepared to do some Googling.

• This course is the first of the four-part Machine Learning Specialization on Coursera.
• Emily Fox , who released the course while a Professor at the University of Washington, has since joined the Department of Statistics of Stanford University.
• Turi, the company behind the software you'll use in this course, that was started by the course co-instructor, Carlos Guestrin, was acquired by Apple in 2016 for $200M.

3. Machine Learning for All (University of London)

My third pick for the best machine learning online course is Machine Learning for All , offered by the University of London on Coursera.

While most other courses either assume prior programming knowledge or teach you programming basics, this course aims to make machine learning accessible to a wider audience. It doesn't require advanced mathematical knowledge or the use of programming languages or machine learning libraries like Python and TensorFlow.

What You’ll Learn

This course starts by explaining what artificial intelligence and machine learning are and how these disciplines are connected.

It discusses various real-world applications of machine learning, including AlphaGo , a machine learning program capable of beating the best Go players in the world. It explains data representation, how to set up a machine learning project, and some of the opportunities and ethical considerations of machine learning.

Finally, the course invites you to implement a machine learning project by collecting data, training a model, and putting it to the test.

By the end of the course, you’ll be equipped with a broad understanding of machine learning, its various uses, and its significance for the future.

You’ll be be familiar with the most important technical concepts that underpin machine learning. You’ll have a high-level grasp of the process of building a machine learning model, from data collection to model evaluation.

And you’ll be prepared to tackle more advanced, theoretical courses on machine learning.

The course is broken down into 4 weeks. Each week involves about 6 hours of work. The course is taught through a mix of video lectures and readings.

Regarding assessments, in most weeks, you’ll complete an hour-long autograded quiz, and in some weeks, you’ll also complete additional practical exercises.

• Dr. Marco Gillies, the course instructor, is also a teacher in the University of London’s online bachelor’s degree in computer science , offered on Coursera.
• This course is the most approachable academic course in the ranking: it’s a bona fide university course, but it's tuned to be suitable to a broad audience.

4. Machine Learning with Python (IBM)

This course offered by IBM on Coursera teaches machine learning through a hands-on approach using Python, which is nowadays the de facto programming language of artificial intelligence.

Beware, this course will throw math at you. If your calculus is rusty, you might want to brush up on that before taking this course.

The course starts by covering machine learning fundamentals and applications in fields such as healthcare, banking, and telecommunications. And it explains the difference between supervised and unsupervised learning, and goes over which type of learning is suitable for which type of task.

Each week is dedicated to one of the broad machine learning tasks — regression, clustering, and classification — and the various methods that can be used to implement them, such as decision trees, support vector machines, and k-means.

By the end of the course, you’ll have covered a lot of ground in terms of the mathematical underpinnings of machine learning. You’ll be familiar with a large number of applications of machine learning in fields ranging from healthcare to high-performance computing.

You’ll be able to implement a tapestry of machine learning algorithms using Python. And you’ll have practiced using machine learning libraries such as scikit-learn and SciPy.

This course is broken down into 4 weeks. Each week involves about 4 hours of work. The course is taught largely through video lessons.

Regarding assessments, each week culminates in a 10-minute practice exercise.

• Saeed Aghabozorgi, one of the course co-instructors is a prolific researcher with over 3300 citations on Google Scholar .
• Besides this course, IBM offers a full-fledged Machine Learning Professional Certificate on Coursera which includes six courses.

If you're interested in this course, you can find more information on the course and how to enroll here .

5. Machine Learning (Georgia Tech)

This course is offered by the Georgia Institute of Technology on Udacity, and it’s also offered as part of Georgia Tech’s Online Master of Computer Science ( OMSCS ).

This course covers machine learning broadly, emphasizing breadth over depth. It favors a high-level approach of machine learning concepts rather than delving into the nitty gritty details of how to implement specific machine learning algorithms.

I’d argue that this course's main strength is its instructional approach.

The course is taught by two instructors, and the lessons are presented as a conversation between them, with one of the instructors playing the role of the student and raising questions.

The exchange is full of humor, which isn’t something that can be said of many machine learning courses.

This course is divided into three broad machine learning tasks.

First, it covers supervised learning, discussing decision trees, regression and classification, and neural networks. Then, it covers unsupervised learning, discussing clustering, feature selection, and randomized optimization. Finally, it covers reinforcement learning, discussing markov decision processes, game theory, and decision making.

By the end of the course, you’ll have a comprehensive understanding of supervised, unsupervised, and reinforcement learning, and the differences between them.

You’ll learn methods tailored to each of these problems. And you’ll be able to implement methods to solve them, interpret the results of these methods, and evaluate their correctness.

The course is broken down into 21 lessons, and each lesson is made up of short videos with in-video quizzes.

The course doesn’t include publicly available projects. Those can only be accessible by students taking the for-credit version of the course via Georgia Tech.

• Charles Isbell and Michael Littman, the course instructors, recorded a machine learning version of Michael Jackson’s Thriller. It's very catchy .
• If you like the conversational approach of this course, these instructors have another similar course on reinforcement learning .

6. Machine Learning Crash Course with TensorFlow APIs (Google)

This course is offered by Google on their developer platform. While most of the courses in this ranking are academic in nature and rather long, this one fits squarely into the category of hands-on introductions to machine learning.

It’s also pragmatic and flexible in that, while it will invite a complete beginner to take the course in full, it will allow those that already have experience in machine learning to instead use the course as a refresher. And that idea is built into the course design, right from the start.

The crash course begins by asking you about your background in machine learning . Depending on your answer, it will orient you toward the appropriate resources, so you can make the best use of your time.

Assuming you’re a complete beginner, you’ll start from square one. So your learning path will cover fundamental machine learning concepts, including regressions, loss functions, and gradient descent.

The course uses TensorFlow, Google’s popular machine learning library. So rapidly the low-level details will be abstracted away by leveraging the library functions.

Some learners could see this as a negative, since you can get away with not understanding how it all works under the hood. But if you're interested in quickly applying machine learning, this crash course should be right up your alley.

A thing to note is that the course also introduces neural networks, a topic many other short machine learning courses prefer to skip or barely touch, since it's a topic worthy of its own separate course.

Google’s crash course, however, is condensed enough to comfortably fit neural nets. But remember, it abstracts away lots of details, so if what you’re after is deep comprehension, you might be better served by another course.

The crash course is broken down into three large sections: (1) machine learning concepts, (2) machine learning engineering, and (3) machine learning systems in the real world.

Each section consists of video lectures presented by Google researchers, readings, and quizzes for self-assessment.

• The crash course has a series of follow-up mini-courses covering topics such as how to frame machine learning problems and how to debug machine learning pipelines .
• D. Scully, the course instructor, is Director of Engineering at Google Brain , which coincidentally was cofounded by Andrew Ng, the instructor of this ranking's top pick.

7. Machine Learning A-Z: Hands-On Python & R in Data Science

True to its name, this Udemy course is a comprehensive but practical introduction to machine learning. It slowly works its way up from data preprocessing to model validation, but glosses over some of the underlying math.

If you want to jump straight into “doing”, this course might be a good fit.

The course starts by covering various types of regression, classification, and clustering models. It discusses reinforcement learning as well as natural language processing, and it covers the fundamentals of artificial neural networks.

The course uses the Python and R programming languages, and the TensorFlow machine learning library.

The course includes over 40 hours of video lessons, interspersed with practical exercises. You’ll build an intuition for each concept and method before applying them to solve concrete problems using dedicated machine learning libraries.

• With over 800K registered learners, Machine Learning A-Z is the most popular machine learning courses on Udemy, and one of the most successful courses on the entire platform.
• Between them, the instructors Kirill Eremenko and Hadelin de Ponteves have created over 80 courses and have close to 3.5M students.

8. Introduction to Machine Learning in Production (DeepLearning.AI)

After launching the machine learning course that tops this ranking and co-founding Coursera, Andrew Ng went on to create another company, DeepLearning.AI.

The company offers a wide variety of courses on machine learning and artificial intelligence, including this course, which covers how to use machine learning in a production environment.

Unlike previous courses, which mainly targeted a general audience (albeit not alway beginners), this course is geared toward learners who already have a solid understanding of machine learning. It targets students who would like to be able to confidently implement end-to-end machine learning pipelines in a professional setting.

This course starts by discussing the lifecycle of a machine learning project and how to deploy production-ready machine learning systems.

Then, the course explains strategies to pick adequate models and train them, as well as some of the pitfalls to avoid when dealing with skewed data sets.

Finally, the course covers how to handle classification problems and how to establish a baseline to assess your model's performance.

This course is broken down into three weeks. Each week involves about 3 hours of work. But remember, it’s an advanced course, so it assumes you already have a machine learning background. Otherwise, the workload could be much higher.

And much like Andrew Ng’s other courses, the course consists of video lessons and readings. Each week ends with several practical exercises using Python and specialized frameworks and libraries like PyTorch and Keras.

• This course is the first of a four-course Coursera Specialization dedicated to MLOps , machine learning engineering for production.
• DeepLearning.AI offers many courses on Coursera, ranging from AI fundamentals to specialized deep learning topics such as generative adversarial networks .

9. Python for Data Science and Machine Learning Bootcamp

As the de facto language of machine learning and AI (at least for now), Python is often a prerequisite of machine learning courses.

Some courses start with a Python refresher before jumping into actual machine learning. But if you’re a novice programmer, a simple refresher may not cut it.

So you either find a Python course and take that first, or you find a course that teaches both machine learning and Python. This is that course.

In the first part of the course, after setting up your development environment, you’ll jump into a Python crash course. You'll learn the fundamentals of the programming language as well as a plethora of widely used libraries, such as NumPy, Pandas, and Matplotlib.

Once you've integrated these skills, you’ll be equipped to tackle the second part of the course, which is entirely dedicated to machine learning.

As usual, you’ll start with regression and work your way up from there, exploring machine learning models and systems ranging from k-means clustering to artificial neural networks.

This course includes over 25 hours of video lessons interspersed with practical exercises. In addition, the course includes numerous references to external material, for those that want to go above and beyond.

• With over 2.6M students on Udemy, the course instructor Jose Portilla is one of Udemy’s most popular instructors.
• While the course covers both Python and Machine learning, if you want to dive deeper into either subjects, Jose Portilla also teaches courses exclusively dedicated to Python and exclusively dedicated to machine learning .

10. Machine Learning for Musicians and Artists (Goldsmith)

Finally, to end this ranking on a high note, my tenth pick is Machine learning for Musicians and Artists , offered by Goldsmith, University of London, through Kadenze.

This course is a bit unconventional: it approaches machine learning it from an artistic angle, from music to visual arts. If that’s actually the sort of mix you’re looking for, then consider this our one and only first pick.

In this course, you’ll learn the fundamentals of machine learning, but you’ll do so by connecting the topic to art, motion, and sound. More specifically, you’ll learn how to use machine learning to interpret human movement, music, and other sources of real-time data.

Don’t worry, this course will also entail learning more pedestrian but essential machine learning concepts, such as regression, classification, and segmentation. It also tackles practical concepts, such as how to configure an end-to-end machine learning pipeline.

The course is broken down into 7 sessions. Each session involves about 8 hours of work.

If you audit the course, you’ll have access to the course material for free, but not the assignments. If you subscribe, you’ll then have access to the assignments and a certificate at the end.

This course uses peer-feedback assignments, meaning your copy will be graded by other students and, in turn, you’ll grade other students to get your grade back.

• This course has an 4.8 / 5.0 rating on Class Central with 80+ ratings, making it one of the highest-rated machine courses on the platform.
• It’s a course that mixes machine learning and art… need I say more!?

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7 Best Machine Learning Courses for 2024 (read this first)

Learn Machine Learning this year from these top courses. Curriculum and learning guide included.

With strong roots in statistics, Machine Learning is becoming one of the most exciting and fast-paced computer science fields. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent.

Chatbots, spam filtering, ad serving, search engines, and fraud detection are among just a few examples of how machine learning models underpin everyday life. Machine learning lets us find patterns and create mathematical models for things that would sometimes be impossible for humans to do.

Unlike data science courses , which contain topics like exploratory data analysis, statistics, communication, and visualization techniques, machine learning courses focus on teaching only the machine learning algorithms, how they work mathematically, and how to utilize them in a programming language.

Now, it’s time to get started. Here’s a TL;DR of the top five machine learning courses this year.

Best 7 Machine Learning Courses in 2024:

• Machine Learning — Coursera
• Deep Learning Specialization — Coursera
• Machine Learning Crash Course — Google AI
• Machine Learning with Python — Coursera
• Advanced Machine Learning Specialization — Coursera*
• Machine Learning — EdX
• Introduction to Machine Learning for Coders — Fast.ai

What makes an excellent machine learning course?

After several years of following the e-learning landscape and enrolling in countless machine learning courses from various platforms, like Coursera, Edx, Udemy, Udacity, and DataCamp, I’ve collected the best available machine learning courses.

Each course in the list is subject to the following criteria. The course should:

• Strictly focus on machine learning.
• Use free, open-source programming languages, such as Python or R.
• Use free, open-source libraries for those languages. Some instructors and providers use commercial packages, so these courses are removed from consideration.
• Contain programming assignments for practice and hands-on experience
• Explain how the algorithms work mathematically
• Be self-paced, on-demand, or available every month or so
• Have engaging instructors and interesting lectures
• Have above-average ratings and reviews from various aggregators and forums

With that, the overall pool of courses gets culled down quickly, but the goal is to help you decide on a course worth your time and energy.

To immerse yourself and learn ML as fast and comprehensively as possible, I believe you should also seek out various books in addition to your online learning. Below are two books that significantly impacted my learning experience and remained at arm's length.

Two Excellent Book Companions

In addition to taking any of the video courses below, if you’re relatively new to machine learning, you should consider reading the following books:

• Introduction to Statistical Learning , which is also available for free online.

This book has detailed, straightforward explanations and examples to boost your overall mathematical intuition for many fundamental machine learning techniques. This book is more on the theory side of things, but it does contain many exercises and examples using the R programming language.

• Hands-On Machine Learning with Scikit-Learn and TensorFlow

A good complement to the previous book since this text focuses more on applying machine learning using Python. Together with any of the courses below, this book will reinforce your programming skills and immediately show you how to apply machine learning to projects.

Now, let’s get to the course descriptions and reviews.

#1 Machine Learning — Coursera

This is the course for which all other machine learning courses are judged. This beginner's course is taught and created by Andrew Ng, a Stanford professor, co-founder of Google Brain, co-founder of Coursera, and the VP that grew Baidu’s AI team to thousands of scientists.

The course uses the open-source programming language Octave instead of Python or R for the assignments. This might be a deal-breaker for some, but Octave is a simple way to learn the fundamentals of ML if you're a complete beginner.

Overall, the course material is extremely well-rounded and intuitively articulated by Ng. The math required to understand each algorithm is completely explained, with some calculus explanations and a refresher for Linear Algebra. The course is fairly self-contained, but some knowledge of Linear Algebra beforehand would help.

Provider: Andrew Ng, Stanford Cost: Free to audit, $79 for Certificate

Course structure:

• Linear Regression with One Variable
• Linear Algebra Review
• Linear Regression with Multiple Variables
• Octave/Matlab Tutorial
• Logistic Regression
• Regularization
• Neural Networks: Representation
• Neural Networks: Learning
• Advice for Applying Machine Learning
• Machine Learning System Design
• Support Vector Machines
• Dimensionality Reduction
• Anomaly Detection
• Recommender Systems
• Large Scale Machine Learning
• Application Example: Photo OCR

All of this is covered over eleven weeks. If you can commit to completing the whole course, you’ll have a good base knowledge of machine learning in about four months .

After that, you can comfortably move on to a more advanced or specialized topic, like Deep Learning, ML Engineering, or anything else that piques your interest.

This is undoubtedly the best course to start with a newcomer.

#2 Deep Learning Specialization — Coursera

Also taught by Andrew Ng, this specialization is a more advanced course series for anyone interested in learning about neural networks and Deep Learning, and how they solve many problems.

The assignments and lectures in each course utilize the Python programming language and use the TensorFlow library for neural networks. This is naturally an excellent follow-up to Ng’s Machine Learning course since you’ll receive a similar lecture style but now will be exposed to using Python for machine learning.

Provider: Andrew Ng, deeplearning.ai Cost: Free to audit, $49/month for Certificate

• Introduction to Deep Learning
• Neural Network Basics
• Shallow Neural Networks
• Deep Neural Networks
• Improving Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
• Structuring Machine Learning Projects
• Convolutional Neural Networks
• Sequence Models

To understand the algorithms presented in this course, you should already be familiar with Linear Algebra and machine learning in general. If you need some suggestions for picking up the math required, see the Learning Guide towards the end of this article.

#3 Machine Learning Crash Course — Google AI

This course comes from Google AI Education, a completely free platform that's a mix of articles, videos, and interactive content.

The Machine Learning Crash Course covers the topics needed to solve ML problems as soon as possible. Like the previous course, Python is the programming language of choice, and TensorFlow is introduced. Each main section of the curriculum contains an interactive Jupyter notebook hosted on Google Colab.

Video lectures and articles are succinct and straightforward, so you'll be able to quickly move through the course at your own pace.

Provider: Google AI

Curriculum (simplified)

• Linear and Logistic Regression
• Classification
• Training and loss
• Reducing Loss - gradient descent, learning rates
• Overfitting
• Training sets, splitting, and validation
• Feature Engineering and cleaning data
• Feature Crosses
• Regularization - L1 and L2, Lambda
• Model performance metrics
• Neural Networks - single and multi-class
• ML Engineering

This is the best option in this list if you have tinkered with ML but are looking to cover all your bases. The course discusses many nuances of machine learning that may otherwise take hundreds of hours to learn serendipitously.

There doesn't seem to be a certificate on completion at the time of writing, so if that's something you're looking for, this course may not be the best fit.

#4 Machine Learning with Python — Coursera

Another beginner course, but this one focuses solely on the most fundamental machine learning algorithms. The instructor, slide animations, and explanation of the algorithms combine very nicely to give you an intuitive feel for the basics.

This course uses Python and is somewhat lighter on the mathematics behind the algorithms. With each module, you’ll get a chance to spool up an interactive Jupyter notebook in your browser to work through the new concepts you just learned. Each notebook reinforces your knowledge and gives you concrete instructions for using an algorithm on real data.

Provider: IBM, Cognitive Class Price: Free to audit, $39/month for Certificate

• Intro to Machine Learning
• Final Project

One of the best things about this course is the practical advice given for each algorithm. When introduced to a new algorithm, the instructor provides you with how it works, its pros and cons, and what sort of situations you should use it in. These points are often left out of other courses and this information is important for new learners to understand the broader context.

#5 Advanced Machine Learning Specialization — Coursera

Russian-ukraine war.

Due to the Russian invasion of Ukraine, Coursera is no longer offering this class until further notice.

This is another advanced series of courses that casts a very wide net. If you are interested in covering as many machine learning techniques as possible, this Specialization is the key to a balanced and extensive online curriculum.

The instruction in this course is fantastic: extremely well-presented and concise. Due to its advanced nature, you will need more math than any other courses listed so far. If you have already taken a beginner course and brushed up on linear algebra and calculus, this is a good choice to fill out the rest of your machine learning expertise.

Much of what’s covered in this Specialization is pivotal to many machine learning projects.

Provider: National Research University Higher School of Economics Cost: Free to audit, $49/month for Certificate

• Intro to Optimization
• Intro to Neural Networks
• Deep Learning for Images
• Unsupervised Representation Learning
• Deep Learning for Sequences
• How to Win Data Science Competitions: Learn from Top Kagglers
• Bayesian Methods for Machine Learning
• Practical Reinforcement Learning
• Deep Learning in Computer Vision
• Natural Language Processing
• Addressing the Large Hadron Collider Challenges by Machine Learning

It takes about 8-10 months to complete this series of courses, so if you start today, in a little under a year, you’ll have learned a massive amount of machine learning and be able to start tackling more cutting-edge applications.

Throughout the months, you will also be creating several real projects that result in a computer learning how to read, see, and play. These projects will be great candidates for your portfolio and will result in your GitHub looking very active to any interested employers.

#6 Machine Learning — EdX

This is an advanced course with the highest math prerequisite out of any other course on this list. You’ll need a very firm grasp of Linear Algebra, Calculus, Probability, and programming. The course has interesting programming assignments in either Python or Octave , but the course doesn’t teach either language.

One of the biggest differences with this course is the coverage of the probabilistic approach to machine learning. If you’ve been interested in reading a textbook, like Machine Learning: A Probabilistic Perspective — which is one of the most recommended data science books in Master’s programs — then this course would be a fantastic complement.

Provider: Columbia Cost: Free to audit, $300 for Certificate

• Maximum Likelihood Estimation, Linear Regression, Least Squares
• Ridge Regression, Bias-Variance, Bayes Rule, Maximum a Posteriori Inference
• Nearest Neighbor Classification, Bayes Classifiers, Linear Classifiers, Perceptron
• Logistic Regression, Laplace Approximation, Kernel Methods, Gaussian Processes
• Maximum Margin, Support Vector Machines (SVM), Trees, Random Forests, Boosting
• Clustering, K-Means, EM Algorithm, Missing Data
• Mixtures of Gaussians, Matrix Factorization
• Non-Negative Matrix Factorization, Latent Factor Models, PCA and Variations
• Markov Models, Hidden Markov Models
• Continuous State-space Models, Association Analysis
• Model Selection, Next Steps

Many of the topics listed are covered in other courses aimed at beginners, but the math isn't watered down here. If you’ve already learned these techniques, are interested in going deeper into the mathematics behind ML, and want to work on programming assignments that derive some of the algorithms, then give this course a shot.

#7 Introduction to Machine Learning for Coders — Fast.ai

Fast.ai produced this excellent, free machine learning course for those that already have roughly a year of Python programming experience.

It's astounding how much time and effort the founders of Fast.ai have put into this course — and other courses on their site. The content is based on the University of San Diego's Data Science program, so you'll find that the lectures are done in a classroom with students, similar to the MIT OpenCourseware style.

The course has many videos, some homework assignments, extensive notes, and a discussion board. Unfortunately, you won't find graded assignments and quizzes or certification upon completion, so Coursera/Edx would be a better route for you if you'd rather have those features.

Much of the course content is applied, so you'll learn how to not only how to use the ML models but also launch them on cloud providers , like AWS.

Provider: Fast.ai

Course Structure:

• Introduction to Random Forests
• Random Forest Deep Dive
• Performance, Validation, and Model Interpretation
• Feature Importance. Tree Interpreter
• Extrapolation and RF from Scratch
• Data Products and Live Coding
• RF From Scratch and Gradient Descent
• Gradient Descent and Logistic Regression
• Regularization, Learning Rates, and NLP
• More NLP and Columnar Data
• Complete Rossmann. Ethical Issues

This course is excellent if you're a programmer who wants to learn and apply ML techniques, but I find there is one drawback: they teach machine learning through the use of their open-source library (called fastai ), which is a layer over other machine learning libraries, like PyTorch.

If you just care about using ML for your project and don't care about learning something like PyTorch, then the fastai library offers convenient abstractions.

Learning Guide

Now that you’ve seen the course recommendations, here’s a quick guide for your learning machine learning journey. First, we’ll touch on the prerequisites for most machine learning courses.

Course Prerequisites

More advanced courses will require the following knowledge before starting:

• Linear Algebra
• Probability
• Programming

These are the general components of being able to understand how machine learning works under the hood. Many beginner courses usually ask for at least some programming and familiarity with linear algebra basics, such as vectors, matrices, and their notation.

The first course in this list, Machine Learning by Andrew Ng, contains refreshers on most of the math you'll need, but it might be challenging to learn machine learning and Linear Algebra if you haven't taken Linear Algebra before at the same time.

If you need to brush up on the math required, check out:

• Matrix Algebra for Engineers from Coursera to cover Linear Algebra
• Fat Chance: Probability from the Ground Up from EdX to cover Probability
• Single Variable Calculus from MIT OpenCourseWare to cover intro Calculus.
• Programming for Everybody course on Coursera to learn Python programming

I’d recommend learning Python since the majority of good ML courses use Python. If you take Andrew Ng’s Machine Learning course, which uses Octave, you should learn Python either during the course or after since you’ll need it eventually. Additionally, another excellent Python resource is dataquest.io , which has many free Python lessons in their interactive browser environment.

After learning the prerequisite essentials, you can start to really understand how the algorithms work.

Fundamental Algorithms

There’s a base set of algorithms in machine learning that everyone should be familiar with and have experience using. These are:

• Linear Regression
• k-Means Clustering
• k-Nearest Neighbors
• Support Vector Machines (SVM)
• Decision Trees
• Random Forests
• Naive Bayes

These are the essentials, but there are many, many more. The courses listed above contain essentially all of these with some variation. Understanding how these techniques work and when to use them will be critical when taking on new projects.

After the basics, some more advanced techniques to learn would be:

• Reinforcement Learning
• Neural Networks and Deep Learning

This is just a start, but these algorithms are what you see in some of the most interesting machine learning solutions, and they’re practical additions to your toolbox.

And just like the basic techniques, with each new tool, you learn you should make it a habit to apply it to a project immediately to solidify your understanding and have something to go back to when in need of a refresher.

Tackle a Project

Learning machine learning online is challenging and extremely rewarding. It’s important to remember that just watching videos and taking quizzes doesn’t mean you’re really learning the material. You’ll learn even more if you have a side project you’re working on that uses different data and has other objectives than the course itself.

As soon as you start learning the basics, you should look for interesting data that you can use while experimenting with your new skills. The courses above will give you some intuition on when to apply certain algorithms, and so it’s a good practice to use them in a project of your own immediately.

Through trial and error, exploration, and feedback, you’ll discover how to experiment with different techniques, how to measure results, and how to classify or make predictions. For some inspiration on what kind of ML project to take on, see this list of examples .

Tackling projects gives you a better high-level understanding of the machine learning landscape. As you get into more advanced concepts, like Deep Learning, there’s virtually an unlimited number of techniques and methods to understand.

Read New Research

Machine learning is a rapidly developing field where new techniques and applications come out daily. Once you’re past the fundamentals, you should be equipped to work through some research papers on a topic that piques your interest.

There are several websites to get notified about new papers matching your criteria. Google Scholar is always a good place to start. Enter keywords like “machine learning” and “Twitter”, or whatever else you’re interested in, and hit the little “Create Alert” link on the left to get emails.

Make it a weekly habit to read those alerts, scan through papers to see if their worth reading, and then commit to understanding what’s going on. If it has to do with a project you’re working on, see if you can apply the techniques to your own problem.

Wrapping Up

Machine learning is incredibly enjoyable and exciting to learn and experiment with, and I hope you found a course above that fits your own journey into this exciting field.

Machine learning makes up one component of Data Science. If you’re also interested in learning about statistics, visualization, data analysis, and more be sure to check out the top data science courses , which is a guide that follows a similar format to this one.

Lastly, if you have any questions or suggestions, feel free to leave them in the comments below.

Thanks for reading, and have fun learning!

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• Machine Learning
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• Foundational courses
• Crash Course

Machine Learning Crash Course with TensorFlow APIs

30+ exercises, lectures from google researchers, real-world case studies, interactive visualizations, some of the questions answered in this course, how does machine learning differ from traditional programming, what is loss, and how do i measure it, how does gradient descent work, how do i determine whether my model is effective, how do i represent my data so that a program can learn from it, how do i build a deep neural network, ready to start practicing machine learning.

Browse Course Material

Course info, instructors.

• Prof. Leslie Kaelbling
• Prof. Tomás Lozano-Pérez
• Prof. Isaac Chuang
• Prof. Duane Boning

Departments

• Electrical Engineering and Computer Science

As Taught In

• Algorithms and Data Structures
• Artificial Intelligence

Introduction to Machine Learning

Course description.

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement …

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.

This course is part of the Open Learning Library , which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling.

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Machine Learning Courses

• Social Sciences

CS50's Introduction to Artificial Intelligence with Python

Learn to use machine learning in Python in this introductory course on artificial intelligence.

Data Science: Machine Learning

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

Machine Learning and AI with Python

Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.

Harvard Business Analytics Program

Designed for aspiring and established leaders in any industry, HBAP equips participants with the machine learning and data analysis tools they need to incorporate innovative tech into their business strategy, at the top levels of their organization.

Leading in Artificial Intelligence: Exploring Technology and Policy (On Campus)

An on campus executive program created jointly by Harvard’s Kennedy School and the Harvard John A. Paulson School of Engineering and Applied Sciences.

Artificial Intelligence in Business: Creating Value with Machine Learning

Leverage new technologies to build value for your organization.

• Español – América Latina
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Master your path

To become an expert in machine learning, you first need a strong foundation in four learning areas : coding, math, ML theory, and how to build your own ML project from start to finish.

Begin with TensorFlow's curated curriculums to improve these four skills, or choose your own learning path by exploring our resource library below.

The four areas of machine learning education

When beginning your educational path, it's important to first understand how to learn ML. We've broken the learning process into four areas of knowledge, with each area providing a foundational piece of the ML puzzle. To help you on your path, we've identified books, videos, and online courses that will uplevel your abilities, and prepare you to use ML for your projects. Start with our guided curriculums designed to increase your knowledge, or choose your own path by exploring our resource library.

Coding skills: Building ML models involves much more than just knowing ML concepts—it requires coding in order to do the data management, parameter tuning, and parsing results needed to test and optimize your model.

Math and stats: ML is a math heavy discipline, so if you plan to modify ML models or build new ones from scratch, familiarity with the underlying math concepts is crucial to the process.

ML theory: Knowing the basics of ML theory will give you a foundation to build on, and help you troubleshoot when something goes wrong.

Build your own projects: Getting hands on experience with ML is the best way to put your knowledge to the test, so don't be afraid to dive in early with a simple colab or tutorial to get some practice.

TensorFlow curriculums

Start learning with one of our guided curriculums containing recommended courses, books, and videos.

Learn the basics of ML with this collection of books and online courses. You will be introduced to ML and guided through deep learning using TensorFlow 2.0. Then you will have the opportunity to practice what you learn with beginner tutorials.

Once you understand the basics of machine learning, take your abilities to the next level by diving into theoretical understanding of neural networks, deep learning, and improving your knowledge of the underlying math concepts.

Learn the basics of developing machine learning models in JavaScript, and how to deploy directly in the browser. You will get a high-level introduction on deep learning and on how to get started with TensorFlow.js through hands-on exercises.

Educational resources

Choose your own learning path, and explore books, courses, videos, and exercises recommended by the TensorFlow team to teach you the foundations of ML.

Reading is one of the best ways to understand the foundations of ML and deep learning. Books can give you the theoretical understanding necessary to help you learn new concepts more quickly in the future.

This introductory book provides a code-first approach to learn how to implement the most common ML scenarios, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes.

This book is a practical, hands-on introduction to Deep Learning with Keras.

Using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—this book helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.

This Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general, and deep learning in particular.

This book provides a theoretical background on neural networks. It does not use TensorFlow, but is a great reference for students interested in learning more.

A hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js.

Written by the main authors of the TensorFlow library, this book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

Online courses

Taking a multi-part online course is a good way to learn the basic concepts of ML. Many courses provide great visual explainers, and the tools needed to start applying machine learning directly at work, or with your personal projects.

DeepLearning.AI

Developed in collaboration with the TensorFlow team, this course is part of the TensorFlow Developer Specialization and will teach you best practices for using TensorFlow.

In this online course developed by the TensorFlow team and Udacity, you'll learn how to build deep learning applications with TensorFlow.

In this four-course Specialization taught by a TensorFlow developer, you'll explore the tools and software developers use to build scalable AI-powered algorithms in TensorFlow.

Google Developers

The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. It features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

In this course from MIT, you will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects and build a career in AI. You will master not only the theory, but also see how it is applied in industry.

You've learned how to build and train models. Now learn to navigate various deployment scenarios and use data more effectively to train your model in this four-course Specialization.

This specialization is for software and ML engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

Learn how you can get more eyes on your cutting edge research, or deliver super powers in your web apps in future work for your clients or the company you work for with web-based machine learning.

Math concepts

To go deeper with your ML knowledge, these resources can help you understand the underlying math concepts necessary for higher level advancement.

A bird's-eye view of linear algebra for machine learning. Never taken linear algebra or know a little about the basics, and want to get a feel for how it's used in ML? Then this video is for you.

Imperial College London

This online specialization from Coursera aims to bridge the gap of mathematics and machine learning, getting you up to speed in the underlying mathematics to build an intuitive understanding, and relating it to Machine Learning and Data Science.

3blue1brown centers around presenting math with a visuals-first approach. In this video series, you will learn the basics of a neural network and how it works through math concepts.

A series of short, visual videos from 3blue1brown that explain the geometric understanding of matrices, determinants, eigen-stuffs and more.

A series of short, visual videos from 3blue1brown that explain the fundamentals of calculus in a way that give you a strong understanding of the fundamental theorems, and not just how the equations work.

This introductory course from MIT covers matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.

This introductory calculus course from MIT covers differentiation and integration of functions of one variable, with applications.

A visual introduction to probability and statistics.

This book provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex world of datasets needed to train models in machine learning.

TensorFlow resources

We've gathered our favorite resources to help you get started with TensorFlow libraries and frameworks specific to your needs. Jump to our sections for TensorFlow.js , TensorFlow Lite , and TFX . You can also browse the official TensorFlow guide and tutorials for the latest examples and colabs.

Machine Learning Foundations is a free training course where you'll learn the fundamentals of building machine learned models using TensorFlow.

This ML Tech Talk is designed for those that know the basics of Machine Learning but need an overview on the fundamentals of TensorFlow (tensors, variables, and gradients without using high level APIs).

This ML Tech Talk includes representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow.

In this series, the TensorFlow Team looks at various parts of TensorFlow from a coding perspective, with videos for use of TensorFlow's high-level APIs, natural language processing, neural structured learning, and more.

Learn to spot the most common ML use cases including analyzing multimedia, building smart search, transforming data, and how to quickly build them into your app with user-friendly tools.

For Javascript

Explore the latest resources at TensorFlow.js .

Get a practical working knowledge of using ML in the browser with JavaScript. Learn how to write custom models from a blank canvas, retrain models via transfer learning, and convert models from Python.

A 3-part series that explores both training and executing machine learned models with TensorFlow.js, and shows you how to create a machine learning model in JavaScript that executes directly in the browser.

Go from zero to hero with web ML using TensorFlow.js. Learn how to create next generation web apps that can run client side and be used on almost any device.

Part of a larger series on machine learning and building neural networks, this video playlist focuses on TensorFlow.js, the core API, and how to use the JavaScript library to train and deploy ML models.

For Mobile & Edge

Explore the latest resources at TensorFlow Lite .

Learn how to build your first on-device ML app through learning pathways that provide step-by-step guides for common use cases including audio classification, visual product search, and more.

Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite in this course, developed by the TensorFlow team and Udacity as a practical approach to model deployment for software developers.

For Production

Explore the latest resources at TFX .

Get a hands-on look at how to put together a production pipeline system with TFX. We'll quickly cover everything from data acquisition, model building, through to deployment and management.

This book walks you through the steps of automating an ML pipeline using the TensorFlow ecosystem. The machine learning examples in this book are based on TensorFlow and Keras, but the core concepts can be applied to any framework.

Expand your production engineering capabilities in this four-course specialization. Learn how to conceptualize, build, and maintain integrated systems that continuously operate in production.

This advanced course covers TFX components, pipeline orchestration and automation, and how to manage ML metadata with Google Cloud.

Human-centered AI

When designing an ML model, or building AI-driven applications, it's important to consider the people interacting with the product, and the best way to build fairness, interpretability, privacy, and security into these AI systems.

Learn how to integrate Responsible AI practices into your ML workflow using TensorFlow.

This guidebook from Google will help you build human-centered AI products. It'll enable you to avoid common mistakes, design excellent experiences, and focus on people as you build AI-driven applications.

This one-hour module within Google's MLCC introduces learners to different types of human biases that can manifest in training data, as well as strategies for identifying, and evaluating their effects.

Join TensorFlow's global community

The 11 best machine learning courses for 2024

The best machine learning courses for helping to kickstart your career in an industry at the cutting-edge of technological innovation

1. Supervised Machine Learning: Regression and Classification

2. machine learning foundations: a case study approach, 3. machine learning for all, 4. machine learning with python, 5. machine learning (georgia tech), 6. machine learning crash course with tensorflow apis, 7. machine learning a-z: ai, python & r + chatgpt bonus [2023], 8. introduction to machine learning in production, 9. python for data science and machine learning bootcamp, 10. machine learning for musicians and artists, 11. intro to machine learning with tensorflow.

If you’re keen on data science or artificial intelligence (AI), enrolling in a machine learning (ML) course could be a significant step forward in your information technology career. The need for engineers with ML expertise is rapidly increasing as organisations aim to integrate and prioritise ML in their products.

Given current industry trends, it’s not surprising that a machine learning engineer can command an average salary of $160,099 per year in the US as of 2023, according to Indeed .

If you want to get into machine learning, there are plenty of online materials that cater for a variety of experiences and skillsets. The below list of the best machine learning courses contains learning materials that we feel are a great place to start, but it offers just a snapshot of what's out there.

Our list of the best machine learning courses has been populated based on a combination of factors, including community reviews, the convenience of each course, and whether they cater for absolute beginners or a current professional looking to up-skill or retrain. Our goal is to provide a broad range of options to give you a good sense the market today.

The 11 best machine learning courses

There are numerous machine learning courses available online. Here are 11 of our favorites.

Provider: DeepLearning.AI (via Coursera)

Price: $49 (£38) per month

Course link: Supervised Machine Learning: Regression and Classification

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Course length: 15 hours

This course, taught by Andrew Ng, provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include supervised learning, unsupervised learning, best practices in machine learning, and case studies and applications.

The course is part of DeepLearning.AI's Machine Learning Specialization, which allows you to further your studies if you wish – although you will need to purchase these separately.

Andrew Ng is widely regarded as one of the foremost experts on machine learning. This Coursera course also has outstanding reviews, and is praised for its structure and case studies that clearly explain ML fundamentals.

Provider: University of Washington (via Coursera)

Price: $79 (£61) per month

Course link: Machine Learning Foundations: A Case Study Approach

Course length: 18 hours

With this comprehensive introduction to machine learning, you’ll gain hands-on experience through practical case studies, learning to predict house prices, analyse sentiment from reviews, retrieve documents, recommend products, and search for images. The course focuses on understanding tasks, matching them to machine learning tools, and assessing output quality. You’ll learn to identify applications of machine learning, apply various techniques, represent data as features, assess model quality, and build applications with machine learning at their core.

The course is also part of a larger specialization offered by the University of Washington, again allowing you to purchase additional individual courses to develop your skills further.

This highly-rated Coursera course is praised for its practical approach using real-world case studies. Reviewers found the hands-on format greatly improved their ML skills.

Provider: University of London (via Coursera)

Price: $59 (£46) - included with Coursera Plus

Course link: Machine Learning for All

Course length: 21 hours

This machine learning course, hosted by the University of London's Prof Marco Gillies, is designed to introduce the fundamentals to those with little to no programming knowledge. It looks at machine learning basics and offers a hands-on approach.

The course includes the opportunity to complete a machine learning project, such as training a computer to recognise images, with the help of user-friendly tools developed by Goldsmiths, the University of London.

This intro course gets rave reviews for making ML accessible for beginners with no programming experience.

Provider: IBM (via Coursera)

Price: $38 (£29)

Course link: Machine Learning with Python

Course length: 12 hours

This module introduces you to the world of Machine Learning using Python, whether you’re looking to advance your data science career or get started in machine learning and deep learning . 

It begins with an introduction to machine learning concepts, including supervised and unsupervised learning, regression, and classification techniques. Emphasis is placed on hands-on learning, working with Python libraries like SciPy and scikit-learn to apply your knowledge through labs and a final project. By the end of the course, you’ll have job-ready skills and a certificate in machine learning.

Like other courses on this list, IBM's machine learning course is part of a wider group of specializations offered on Coursera, specifically AI engineering and Data Science Professional - both of which offer certificates upon completion, at an extra cost.

IBM's course is valued for emphasizing practical application of ML using Python. Hands-on labs and projects provide learners with job-ready skills, and we also appreciate how flexible the course is when it comes to potential jobs routes and further specializations.

Provider: Georgia Tech (via Udacity)

Price: $249 (£194) per month

Course link: Machine Learning

Course length: 16 weeks

This course, CS7641 at Georgia Tech, is part of the Online master’s degree (OMS) program. It covers the area of Artificial Intelligence concerned with computer programs that improve their performance through experience. 

The first part covers Supervised Learning, enabling computers to recognise voice, filter spam, and more. The second part covers Unsupervised Learning, used by companies like Netflix and Amazon to make predictions, while the concluding section teaches Reinforcement Learning algorithms for designing self-learning agents.

As part of Georgia Tech's respected master's program, this course offers proven ML training, with reviewers appreciating the theory combined with practical application.

Provider: Google

Price: Free

Course link: Machine Learning Crash Course with TensorFlow APIs

This course introduces machine learning using TensorFlow APIs. The system provides a fast-paced, practical introduction to machine learning, featuring video lectures, real-world case studies, and hands-on practice exercises. 

It includes 25 lessons, 30+ exercises, and takes approximately 15 hours to complete. The course covers key machine learning concepts and best practices, with lectures from Google researchers and interactive visualisations.

This course makes the list largely because it's a free opportunity to hear directly from Google experts. It does a great job of covering ML basics with TensorFlow, and the fast-paced lessons and interactive exercises have proven effective among reviewers.

Provider: Kirill Eremenko et al (via Udemy)

Price: $109 (£60)

Course link: Machine Learning A-Z: AI, Python & R + ChatGPT Bonus

Course length: 43 hours

The course aims to provide students with a strong intuition of many Machine Learning models, enabling them to make accurate predictions, powerful analyses, and robust models. 

The course covers a range of topics, including supervised and unsupervised learning, reinforcement learning , natural language processing , deep learning, and dimensionality reduction. Students will learn how to choose the appropriate ML model for each problem and apply their knowledge to create added value for their business or personal projects.

Why choose this course? With outstanding reviews, this Udemy course is praised for clearly explaining ML concepts and real-world applications. The comprehensive curriculum provides strong ML foundations.

Course link: Introduction to Machine Learning in Production

Course length: 10 hours

This course, provided by Andrew Ng and DeepLearning.AI, covers the critical components of the ML lifecycle and pipeline and teaches students how to identify and solve problems for structured, unstructured, small, and big data. 

The course also covers topics such as human-level performance, concept drift, model baselines, project scoping and design, and ML deployment challenges.

Part of DeepLearning.AI's highly-rated MLOps Specialization certificate, this course is a good option for those looking for more specialist teaching in the ML lifecycle discipline.

Provider: Jose Portilla (via Udemy)

Price: $100 (£70)

Course link: Python for Data Science and Machine Learning Bootcamp

Course length: 25 hours

This course is hosted by Jose Portilla, a renowned Udemy instructor who has a number of highly rated data science courses on the platform. Here he teaches students how to use Python for data science and machine learning. 

It covers a range of topics, including programming with Python, data analysis with Pandas and NumPy, data visualisation with Matplotlib and Seaborn, and machine learning with Scikit-Learn. The course provides hands-on practice through detailed code notebooks and HD video lectures.

This hands-on Udemy bootcamp gets rave reviews for effectively teaching Python coding and ML application.

Provider: Goldsmiths, University of London (via Kadenze)

Price: $20 per month

Course link: Machine Learning for Musicians and Artists

Course length: 56 hours

Fundamental machine learning techniques are taught in this course, which can be used to make sense of human gestures, musical audio, and other real-time data. The course focuses on learning about algorithms, software tools, and best practices that can be immediately employed in creating new real-time systems in the arts. 

Topics covered include classification, regression, segmentation, and the "machine learning pipeline". The course also introduces off-the-shelf tools for machine learning and feature extraction techniques for music, dance, gaming, and visual art.

This course is perfect for those interested in the creative arts but that also need something that simplifies complex ML concepts.

Provider: Josh Bernhard et al (via Udacity)

Course link: Intro to Machine Learning with TensorFlow

Course length: 2 months

This course is a practical introduction to machine learning using TensorFlow, a popular framework for building and deploying ML models. The course covers foundational ML techniques, such as data manipulation, supervised and unsupervised learning, and deep learning. 

Students will learn how to use TensorFlow to implement various ML algorithms and apply them to real-world problems. The course also includes real-world case studies, interactive visualisations, and hands-on practice exercises. 

The course is intended for students with intermediate Python programming knowledge and basic knowledge of probability and statistics.

Udacity's project-based course is praised for building ML skills through practical TensorFlow projects. Reviewers also value the employable skills the curriculum offers.

Rene Millman is a freelance writer and broadcaster who covers cybersecurity, AI, IoT, and the cloud. He also works as a contributing analyst at GigaOm and has previously worked as an analyst for Gartner covering the infrastructure market. He has made numerous television appearances to give his views and expertise on technology trends and companies that affect and shape our lives. You can follow Rene Millman on  Twitter .

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Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.   

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.  

Throughout the coursework, you will:

• Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
• Learn how to approach and solve large-scale data science problems.
• Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world. 

A Powerful Certificate. Conveniently Offered. 

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known. 

For Today’s Problem Solvers

This leading certificate program is best suited for:

• Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
• Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.   

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

At a Glance

Start Date May 2024

Application Deadlines Final*: April 9, 2024

*A limited number of partial scholarships are still available. Apply by the final deadline to receive initial consideration for these awards.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more.  Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction. 

Number One  in the nation for our artificial intelligence programs.

Number One  in the nation  for our programming language courses.

Number Four  in the nation for the caliber of our computer science programs.

Mini-Course on: Physics Informed Machine Learning with Pytorch and Julia

5 Day Mini-Course on: Physics Informed Machine Learning with Pytorch and Julia

Instructors: Arvind Mohan and Nicholas Lubbers, Computational, Computer, and Statistical Division, Los Alamos National Laboratory

Day 1: The killer feature: Automatic Differentiation

           0 - Theory of automatic differentiation 

           1 - Example Notebooks: Introduction to Pytorch

           2 - Example Notebooks:  Tape-based Automatic Differentiation

Spotify tests video courses to teach everything from music production to Excel

Spotify has carved out a business for itself in music streaming, podcast entertainment and audiobooks. Now, in its ongoing efforts to get its 600 million+ users to spend more time and money on its platform, Spotify is spinning up a new line of content: e-learning.

Starting with a rollout in the U.K., Spotify is testing the waters for an online education offering of freemium video courses. Produced in partnership with third parties like the BBC and Skillshare, at least two lessons will be free, with the cost of a total course ranging from £20 to £80 on average. The prices will be the same, regardless of whether you are a basic or premium user, at least for now.

Mohit Jitani, the London-based product director for the education business, said in an interview that pricing choices were part of what it’s testing before considering how to roll out more widely. “With this launch, we’re trying to understand the demand first,” he said. “Then we optimize how we can make it more compelling and exciting.”

The content will live in both Spotify’s home and browse tabs (under “Courses”), and it’s accessible on the web as well as via the Spotify mobile app.

The courses are pitched somewhere between YouTube, Master Class and LinkedIn Learning: Videos in the current catalog cover a wide range of subjects, from music production through to learning how to use Excel, as well as lessons on — you guessed it — how to create online learning lessons to turn musicians and others into “education creators.”

Unsurprising for a market estimated to have been worth more than $315 billion in 2023 , there are plenty of online learning sites on the web these days, some of which have been innovators in interactive content and other media formats — you can even find a number of startups aspiring to be the “Spotify for education” if you Google that term — Spotify’s educational push is focused around one-directional, on-demand video.

Some courses appear to have supplementary material, although that will be more in the realm of extra documents rather than tests or other interactions. Jitani declined to comment on whether Spotify would launch any kind of interaction or gamification in the future — or, indeed, if games of any kind are on its roadmap right now.

The first partners for Courses are Skillshare (which will focus on creatives), PLAYvirtuoso (music industry courses), BBC Maestro (Master Class-esque) and Thinkific (for those inspired to build their skills into online learning classes of their own).

Spotify, Jitani said, would be looking to curate which courses it offers, and it will base curation on what people are already listening to and searching for on its platform. There appears to be no limit, though. If you look at the catalogs of these respective providers, you’ll see that the topics cover a pretty wide breadth — and bread .

“We’ll learn a lot about what people are actually interested in [and] we will start getting a lot of segments around that,” Jitani said. “And then we’ll go and find… the best content.”

Third-party publishers own the videos and license them to Spotify, but they will be hosted and purchased on Spotify itself. In terms of revenue share, the creator, publisher and Spotify will all get a share of the sales, with content partners overseeing payments to creators.

Spotify isn’t specifying what kind of cut will be going to whom, nor whether it will potentially offer any kind of discount or other benefit to users who are already premium subscribers on the platform.

Why education? Why the UK?

The move points to Spotify’s strategy to continue diversifying its business, while also aiming to build a path to more consistent profitability and stronger margins. It’s picked the U.K. for this, Jitani said, because it’s a huge market for the company and is already one of the most engaged in the world.

Financially, Spotify continues to see a lot of ups and downs in the current market. It went through three rounds of layoffs last year; and it has been unprofitable more than profitable over the years, most recently posting a net loss of $81 million   in its quarterly earnings in February .

Yes, the dry realms of online learning and professional development might sound like a reach for a company still best known for music streaming, but there are three areas where it makes some sense.

With its podcasting business continuing to grow, Spotify is picking up a lot of data on what people are doing on the platform, and it’s finding a close correlation between some of the most popular podcasts on Spotify and education content.

Around half of Spotify Premium subscribers have listened to education or self-help themed podcasts, Spotify says. Spotify can use the same kind of recommendation surfacing that it uses for music and podcasts to cross-promote. Think of a podcast with a “business guru” now recommending a paid course with that person. Spotify’s making a bet that one will help sell the other.

Alongside this, Spotify has long been working on tools for creators to help them manage and grow their earnings. Offering educational content aimed at running a business, or improving your music production, fits with that.

Third of all, there is the video element. Spotify’s been trying to get deeper into video for the better part of a decade.

That hasn’t translated to being a YouTube or Netflix rival yet. Video was mentioned a grand total of one time in the company’s last earning call, where CEO Daniel Ek vaguely described video podcasting as “growing in a healthy way.” But it launched music videos in select markets earlier this month, and now we have an earnest effort in educational videos. It may find its groove yet.

Why Taiwan Was So Prepared for a Powerful Earthquake

Decades of learning from disasters, tightening building codes and increasing public awareness may have helped its people better weather strong quakes.

Search-and-rescue teams recover a body from a leaning building in Hualien, Taiwan. Thanks to improvements in building codes after past earthquakes, many structures withstood Wednesday’s quake. Credit...

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By Chris Buckley ,  Meaghan Tobin and Siyi Zhao

Photographs by Lam Yik Fei

Chris Buckley reported from the city of Hualien, Meaghan Tobin from Taipei, in Taiwan.

• April 4, 2024

When the largest earthquake in Taiwan in half a century struck off its east coast, the buildings in the closest city, Hualien, swayed and rocked. As more than 300 aftershocks rocked the island over the next 24 hours to Thursday morning, the buildings shook again and again.

But for the most part, they stood.

Even the two buildings that suffered the most damage remained largely intact, allowing residents to climb to safety out the windows of upper stories. One of them, the rounded, red brick Uranus Building, which leaned precariously after its first floors collapsed, was mostly drawing curious onlookers.

The building is a reminder of how much Taiwan has prepared for disasters like the magnitude-7.4 earthquake that jolted the island on Wednesday. Perhaps because of improvements in building codes, greater public awareness and highly trained search-and-rescue operations — and, likely, a dose of good luck — the casualty figures were relatively low. By Thursday, 10 people had died and more than 1,000 others were injured. Several dozen were missing.

“Similar level earthquakes in other societies have killed far more people,” said Daniel Aldrich , a director of the Global Resilience Institute at Northeastern University. Of Taiwan, he added: “And most of these deaths, it seems, have come from rock slides and boulders, rather than building collapses.”

Across the island, rail traffic had resumed by Thursday, including trains to Hualien. Workers who had been stuck in a rock quarry were lifted out by helicopter. Roads were slowly being repaired. Hundreds of people were stranded at a hotel near a national park because of a blocked road, but they were visited by rescuers and medics.

On Thursday in Hualien city, the area around the Uranus Building was sealed off, while construction workers tried to prevent the leaning structure from toppling completely. First they placed three-legged concrete blocks that resembled giant Lego pieces in front of the building, and then they piled dirt and rocks on top of those blocks with excavators.

“We came to see for ourselves how serious it was, why it has tilted,” said Chang Mei-chu, 66, a retiree who rode a scooter with her husband Lai Yung-chi, 72, to the building on Thursday. Mr. Lai said he was a retired builder who used to install power and water pipes in buildings, and so he knew about building standards. The couple’s apartment, near Hualien’s train station, had not been badly damaged, he said.

“I wasn’t worried about our building, because I know they paid attention to earthquake resistance when building it. I watched them pour the cement to make sure,” Mr. Lai said. “There have been improvements. After each earthquake, they raise the standards some more.”

It was possible to walk for city blocks without seeing clear signs of the powerful earthquake. Many buildings remained intact, some of them old and weather-worn; others modern, multistory concrete-and-glass structures. Shops were open, selling coffee, ice cream and betel nuts. Next to the Uranus Building, a popular night market with food stalls offering fried seafood, dumplings and sweets was up and running by Thursday evening.

Earthquakes are unavoidable in Taiwan, which sits on multiple active faults. Decades of work learning from other disasters, implementing strict building codes and increasing public awareness have gone into helping its people weather frequent strong quakes.

Not far from the Uranus Building, for example, officials had inspected a building with cracked pillars and concluded that it was dangerous to stay in. Residents were given 15 minutes to dash inside and retrieve as many belongings as they could. Some ran out with computers, while others threw bags of clothes out of windows onto the street, which was also still littered with broken glass and cement fragments from the quake.

One of its residents, Chen Ching-ming, a preacher at a church next door, said he thought the building might be torn down. He was able to salvage a TV and some bedding, which now sat on the sidewalk, and was preparing to go back in for more. “I’ll lose a lot of valuable things — a fridge, a microwave, a washing machine,” he said. “All gone.”

Requirements for earthquake resistance have been built into Taiwan’s building codes since 1974. In the decades since, the writers of Taiwan’s building code also applied lessons learned from other major earthquakes around the world, including in Mexico and Los Angeles, to strengthen Taiwan’s code.

After more than 2,400 people were killed and at least 10,000 others injured during the Chi-Chi quake of 1999, thousands of buildings built before the quake were reviewed and reinforced. After another strong quake in 2018 in Hualien, the government ordered a new round of building inspections. Since then, multiple updates to the building code have been released.

“We have retrofitted more than 10,000 school buildings in the last 20 years,” said Chung-Che Chou, the director general of the National Center for Research on Earthquake Engineering in Taipei.

The government had also helped reinforce private apartment buildings over the past six years by adding new steel braces and increasing column and beam sizes, Dr. Chou said. Not far from the buildings that partially collapsed in Hualien, some of the older buildings that had been retrofitted in this way survived Wednesday’s quake, he said.

The result of all this is that even Taiwan’s tallest skyscrapers can withstand regular seismic jolts. The capital city’s most iconic building, Taipei 101, once the tallest building in the world, was engineered to stand through typhoon winds and frequent quakes. Still, some experts say that more needs to be done to either strengthen or demolish structures that don’t meet standards, and such calls have grown louder in the wake of the latest earthquake.

Taiwan has another major reason to protect its infrastructure: It is home to the majority of production for the Taiwan Semiconductor Manufacturing Company, the world’s largest maker of advanced computer chips. The supply chain for electronics from smartphones to cars to fighter jets rests on the output of TSMC’s factories, which make these chips in facilities that cost billions of dollars to build.

The 1999 quake also prompted TSMC to take extra steps to insulate its factories from earthquake damage. The company made major structural adjustments and adopted new technologies like early warning systems. When another large quake struck the southern city of Kaohsiung in February 2016, TSMC’s two nearby factories survived without structural damage.

Taiwan has made strides in its response to disasters, experts say. In the first 24 hours after the quake, rescuers freed hundreds of people who were trapped in cars in between rockfalls on the highway and stranded on mountain ledges in rock quarries.

“After years of hard work on capacity building, the overall performance of the island has improved significantly,” said Bruce Wong, an emergency management consultant in Hong Kong. Taiwan’s rescue teams have come to specialize in complex efforts, he said, and it has also been able to tap the skills of trained volunteers.

Taiwan’s resilience also stems from a strong civil society that is involved in public preparedness for disasters.

Ou Chi-hu, a member of a group of Taiwanese military veterans, was helping distribute water and other supplies at a school that was serving as a shelter for displaced residents in Hualien. He said that people had learned from the 1999 earthquake how to be more prepared.

“They know to shelter in a corner of the room or somewhere else safer,” he said. Many residents also keep a bag of essentials next to their beds, and own fire extinguishers, he added.

Around him, a dozen or so other charities and groups were offering residents food, money, counseling and childcare. The Tzu Chi Foundation, a large Taiwanese Buddhist charity, provided tents for families to use inside the school hall so they could have more privacy. Huang Yu-chi, a disaster relief manager with the foundation, said nonprofits had learned from earlier disasters.

“Now we’re more systematic and have a better idea of disaster prevention,” Mr. Huang said.

Mike Ives contributed reporting from Seoul.

Chris Buckley , the chief China correspondent for The Times, reports on China and Taiwan from Taipei, focused on politics, social change and security and military issues. More about Chris Buckley

Meaghan Tobin is a technology correspondent for The Times based in Taipei, covering business and tech stories in Asia with a focus on China. More about Meaghan Tobin

Siyi Zhao is a reporter and researcher who covers news in mainland China for The Times in Seoul. More about Siyi Zhao

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