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If you’re interested in a data career, it’s important to become familiar with machine learning. With data analysis, you can analyze relevant historical data to answer business questions. But with machine learning, you can take this a step further by building models that can predict future trends based on the available data.
To help you get started with machine learning we’ve compiled a list of free courses at universities like MIT, Harvard, Stanford, and UMich. I recommend sifting through the contents of the courses to get a feel for what they cover. And then based on what you’re interested in learning, you can choose to work through one or more of these courses.
Let’s get started!
1. Introduction to Machine Learning – MIT
The Introduction to Machine Learning course from MIT covers a range of ML topics in considerable depth. You can access the course contents including the exercises and practice labs for free on MIT Open Learning Library.
From the basics of machine learning to ConvNets and recommender systems, here’s a list of topics that this course covers:
- Linear classifiers
- Perceptrons
- Margin maximization
- Regression
- Neural networks
- Convolutional neural networks
- State machines and Markov Decision Processes
- Reinforcement learning
- Recommended systems
- Decision trees and nearest neighbors
Link: Introduction to Machine Learning
2. Data Science: Machine Learning – Harvard
Data Science: Machine Learning is another course where you’ll get to learn machine learning fundamentals by working on practical applications such as movie recommendation systems.
The course goes over the following topics:
- Basics of machine learning
- Cross-validation and overfitting
- Machine learning algorithms
- Recommendation systems
- Regularization
Link: Data Science: Machine Learning
3. Applied Machine Learning with Python – University of Michigan
Applied Machine Learning in Python is offered by the University of Michigan on Coursera. You can sign up for free on Coursera and access the course contents for free (audit track).
This is a comprehensive course that focuses on popular machine learning algorithms along with their scikit-learn implementation. You’ll work on simple programming exercises and projects using scikit-learn. Here’s the list of topics this course covers:
- Introduction to machine learning and scikit-learn
- Linear regression
- Linear classifiers
- Decision trees
- Model evaluation and selection
- Naive Bayes, Random forest, Gradient boosting
- Neural networks
- Unsupervised learning
This course is part of the Applied Data Science with Python specialization offered by the University of Michigan on Coursera.
Link: Applied Machine Learning in Python
4. Machine Learning – Stanford
As a data scientist, you should also be comfortable building predictive models. Learning how machine learning algorithms work and being able to implement them in Python can, therefore, be very helpful.
CS229: Machine Learning at Stanford university is one of the highly recommended ML courses. This course lets you explore the different learning paradigms: supervised, unsupervised, and reinforcement learning. Additionally, you’ll also learn about techniques like regularization to prevent overfitting and build models that generalize well.
Here’s an overview of the topics covered:
- Supervised learning
- Unsupervised learning
- Deep learning
- Generalization and regularization
- Reinforcement learning and control
Link: Machine Learning
5. Statistical Learning with Python – Stanford
The Statistical Learning with Python course covers all the contents of the ISL with Python book. Working through the course and using the book as a companion, you’ll learn essential tools for data science and statistical modeling.
Here is a list of the key areas that this course covers:
- Linear regression
- Classification
- Resampling
- Linear model selection
- Tree-based methods
- Unsupervised learning
- Deep learning
Link: Statistical Learning with Python
Wrapping Up
I hope you found this list of free machine learning courses from top universities useful. Whether you want to work as a machine learning engineer or want to explore machine learning research, these courses will help you gain the foundations.
Here are a couple of related resources you might find helpful:
Happy learning!
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.
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