5 Free Courses to Understand Machine Learning Algorithms

Machine learning has rapidly become a cornerstone in modern technology, enabling systems to learn and improve automatically through experience. 

Whether you’re venturing into AI, data science, or any technology-driven industry, understanding machine learning algorithms is critical. To help you navigate this complex subject, we’ve compiled five free online courses that will give you a solid foundation in machine learning algorithms.

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Course 1: Machine Learning by Stanford University (Coursera)

• Instructor: Andrew Ng
• Level: Beginner to Intermediate
• Platform: Coursera

This course is one of the most well-known and widely recommended introductions to machine learning. Andrew Ng, a co-founder of Google Brain and former head of Baidu AI Group, walks you through the core concepts of machine learning, data mining, and statistical pattern recognition.

The course is structured to gradually introduce you to machine learning techniques like linear regression, logistic regression, neural networks, and more. What sets this course apart is its hands-on approach, with real-world applications such as speech recognition, web search, and self-driving cars. You’ll also gain experience with the Octave programming environment, which allows for easy implementation of algorithms.

This course provides a holistic view of machine learning, making it a perfect starting point for anyone serious about diving deep into algorithms. You can access it for free, although there’s an option to pay for a certificate.

Course 2: Introduction to Machine Learning for Coders (fast.ai)

• Instructors: Jeremy Howard, Sylvain Gugger
• Level: Intermediate
• Platform: fast.ai

This course is designed for people who already have some coding experience, particularly in Python. If you’re a programmer looking to transition into machine learning, this is an ideal starting point. 

Created by fast.ai, this course focuses on practical coding experience and rapidly moves into implementing machine learning algorithms without getting bogged down by unnecessary theory.

What’s great about fast.ai’s approach is its emphasis on building models from day one. The course begins by showing you how to construct a state-of-the-art image classifier using deep learning and then goes on to explain the algorithms behind it. 

As you progress, you’ll dive into essential machine learning concepts like gradient descent, decision trees, and convolutional neural networks (CNNs).

This hands-on approach ensures you learn how to work with machine learning algorithms in real-life applications, such as image recognition, language processing, and more.

Course 3: Machine Learning Crash Course by Google

• Instructor: Google AI Experts
• Level: Beginner
• Platform: Google Developers

If you’re looking for a self-paced and beginner-friendly introduction to machine learning, this crash course from Google is an excellent option. 

Developed by experts from Google’s AI team, this course provides both the theory and the practical aspects of machine learning. It’s an excellent way to quickly grasp core algorithms like linear regression, classification, clustering, and neural networks.

The course includes interactive lessons, real-world case studies, and coding exercises using TensorFlow, one of the most widely used machine learning frameworks today. The use of TensorFlow also prepares you for future work in more complex machine learning environments, as you become familiar with an industry-standard tool.

One of the highlights is the hands-on exercises, which give you immediate feedback and allow you to experiment with algorithms as you go. You’ll also learn the basic math behind these algorithms, ensuring you understand how they work at their core.

Course 4: Applied Machine Learning with Python (University of Michigan)

• Instructor: Kevyn Collins-Thompson
• Level: Intermediate
• Platform: Coursera

This course, offered by the University of Michigan, is ideal for Python enthusiasts who want to focus on the application of machine learning algorithms. 

As part of the Applied Data Science with Python specialization, it offers a strong focus on practical aspects of machine learning while using Python’s popular machine learning libraries, such as Scikit-learn and Pandas.

Through this course, you’ll explore different supervised and unsupervised machine learning algorithms, including decision trees, random forests, k-nearest neighbors (KNN), and support vector machines (SVM).

In addition to building models, the course demonstrates how machine learning can be applied to real-world scenarios, such as organizing sub-accounts for budgeting, customer segmentation, tracking inventory across multiple locations, and managing tasks in project workflows.

By the end of the course, you’ll have a solid understanding of how to use Python to build models and solve ML problems in a practical, real-world context.

Course 5: Elements of AI by the University of Helsinki and Reaktor

• Instructor: Teemu Roos
• Level: Beginner
• Platform: ElementsofAI.com

If you’re looking for a course that doesn’t dive too deeply into coding but still offers a comprehensive understanding of machine learning algorithms, the Elements of AI is a great choice. This course, created by the University of Helsinki and Reaktor, is designed for absolute beginners, offering a broad introduction to AI and machine learning.

The Elements of AI is split into six chapters that take you through the key concepts of AI, machine learning, neural networks, and decision-making algorithms. 

It avoids overwhelming beginners with complex math or heavy coding, making it ideal for those coming from non-technical backgrounds. The course provides simple yet effective explanations of machine learning algorithms and the principles that govern them, using real-world examples like language processing and facial recognition.

One of the benefits of this course is its flexibility; you can go at your own pace, and it’s available in multiple languages. With its clear and engaging presentation, the Elements of AI is perfect for anyone looking to understand the broader context of machine learning algorithms without getting lost in technical details.

Conclusion

 
These five free courses cover a broad range of machine learning algorithms, from beginner to intermediate levels, offering a variety of perspectives and teaching styles. Whether you’re just starting or looking to deepen your understanding of machine learning algorithms, these resources provide invaluable tools for advancing your knowledge.

Each course emphasizes practical applications, ensuring that by the end, you’ll have both theoretical knowledge and the hands-on experience necessary to apply machine learning algorithms in real-world scenarios. 

With these resources at your disposal, you’ll be well on your way to mastering machine learning algorithms and pushing forward in your career or academic pursuits.
 
 

Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.