5 free university courses to learn machine learning

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If you are interested in a career in data, it is important to familiarize yourself with machine learning. With data analytics, you can analyze relevant historical data to answer business questions. But with machine learning, you can go a step further and create models that can predict future trends based on 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 looking at the content of the courses to get an idea of ​​what they cover. And then, depending on what you're interested in learning, you can choose to work on one or more of these courses.

Let us begin!

1. Introduction to machine learning – MIT

He Introduction to machine learning The MIT course covers a variety of ML topics in considerable depth. You can access course content, including exercises and practice labs, for free in the MIT Open Learning Library.

From the basics of machine learning to ConvNets and recommender systems, here is a list of topics 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 learn the fundamentals of machine learning by working on practical applications such as movie recommendation systems.

The course addresses the following topics:

• Machine Learning Basics
• 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 It is offered by the University of Michigan on Coursera. You can register for free on Coursera and access course content for free (audit trail).

This is a comprehensive course that focuses on popular machine learning algorithms along with their scikit-learn implementation. You will work on simple programming exercises and projects using scikit-learn. Here is the list of topics this course covers:

• Introduction to machine learning and scikit-learn
• Linear regression
• Linear classifiers
• Decision trees
• Evaluation and selection of models.
• 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 creating predictive models. Therefore, learning how machine learning algorithms work and being able to implement them in Python can be very useful.

CS229: Machine Learning at Stanford University is one of the most recommended ML courses. This course allows you to explore the different learning paradigms: supervised, unsupervised and reinforcement learning. Additionally, you will also learn about techniques such as regularization to avoid overfitting and create models that generalize well.

Here is 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

He Statistical learning with Python The course covers all the contents of the ISL with Python book. Throughout the course and using the book as a complement, you will learn essential tools for data science and statistical modeling.

Here is a list of the key areas this course covers:

• Linear regression
• Classification
• Resampling
• Linear model selection
• Tree-based methods
• Unsupervised learning
• Deep learning

Link: Statistical learning with Python

Ending

I hope you found this list of free machine learning courses from top universities helpful. Whether you want to work as a machine learning engineer or explore machine learning research, these courses will help you get the basics.

Here are a couple of related resources that you may find helpful:

Happy learning!

twitter.com/balawc27″ rel=”noopener”>Bala Priya C. is a developer and technical writer from India. He enjoys working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. He likes to read, write, code and drink coffee! Currently, he is working to learn and share his knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource descriptions and coding tutorials.