Top online resources to learn Active Learning

Their tutorial on active learning in ML breaks down the principles of the concept along with real-life examples and mathematical explanations.

A key requirement of machine learning is to label the data correctly to ensure the best results, but the process is long and time-consuming. This also brings about an issue when dealing with extremely large data sets in unsupervised or semi-supervised learning. The saviour here is active learning with strategies that assist developers in prioritising the data and selecting the most useful samples to label to have the highest training impact. Furthermore, it promises to reduce the samples needed by choosing the right examples. 

Various strategies can be used depending on the applications and needs of the model. However, when it comes to learning active learning, the practice is generally a part of bigger machine learning modules, which is why we have created a one-stop guide to mastering active learning online through resources varying from online video tutorials to blog posts and academic papers. 


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  1. Active (Machine) Learning – Computerphile

Computerphile is a popular YouTube channel that discusses computer science-related topics. Their tutorial on active learning is taught by Dr Michel Valstar, who holds a PhD in Computing and is currently a professor at the University of Nottingham. The tutorial is a foundational element for the basics of active learning, taught through diagrams and illustrations of the concepts. 

  1. ICML 2019 Tutorial Session on Active Learning from Theory to Practice

ICML, the International Conference on Machine Learning, is one of the fastest-growing AI conferences that discuss the latest academic papers. During their 2019 conference, Robert Nowak and Steve Hanneke taught the basics of active learning theory and the popular algorithms to apply (the video is now available online). In addition, the tutorial focuses on sound active learning algorithms and how they can be used to reduce the labels on training data. Robert Nowak holds the Nosbusch Professorship in Engineering at the University of Wisconsin-Madison. Steve Hanneke is a Research Assistant Professor at the Toyota Technological Institute in Chicago, specialising in AI and ML. 

  1. Active Learning in ML

Applied AI is a great resource for learning AI/ML online through core concepts and real-life applications. The channel’s collective views cross 12 million and are popular for the basic concepts’ thorough teachings. Their tutorial on active learning in ML breaks down the principles of the concept along with real-life examples and mathematical explanations. 

  1. Jan Freyberg: Active learning in the interactive python environment

PyData is an educational program of NumFOCUS, a US-based not for profit organisation that provides a forum for the international community of data science to share their ideas through conferences. Speaking at one of their events is Jan Freyberg, a machine learning software engineer at Google Health. In a detailed talk, Freyberg discusses active learning in the interactive Python environment, given the ease and comfort in the ecosystem. 

  1. Active Learning: Machine Learning Made Simple

Devansh is a Computer Science and Computational Math Double Major at the Rochester Institute of Technology. Through this YouTube tutorial, he comprehensively discusses the basics of active learning, its works and compares it to SSL and GANs. He further explains the concept in detail regarding its use and active learning’s acquisition function. 

  1. Machine Learning | Active Learning

Ranji Raj, holding a masters degree in data science, takes on Youtube to publish tutorials and classwork related to machine learning. His video on active learning gives an in-depth introduction to the subject while discussing important concepts through diagrams and demonstrations. Raj also has consequent coursework on his GitHub page for data scientists interested in learning further.

  1. WEBINAR | What does “active learning” mean in AI? Save time and money on data annotation | Beginner

Scaleway is a French cloud computing company that creates Youtube videos consisting of short machine learning tutorials and real-world applications. In their webinar on active learning, the company collaborated with Kairntech, an AI modelling and dataset creation platform, to discuss the various applications of active learning. The video discusses training datasets and how active learning can be applied for classification. It also glossed over common issues and how to overcome them. 

Blog tutorials

  1. Active Learning Tutorial

Ori Cohen is a PhD holder in CS, currently working as a senior director of data science at New Relic. His ‘Towards Data Science’ blog post on active learning is an extensive tutorial that discusses the various scenarios possible while using active learning, the algorithms that can be used, the sample selection methods and the codings used for all. 

  1. Guide to Active Learning in Machine Learning (ML) – DataCamp

A blog post on Data Camp, an online interactive learning platform, explains in depth the A-Zs of active learning in a moderate level of difficulty. The tutorial discusses the concept in detail with definitions, examples and visuals, and teaches how one can apply active learning on their datasets through a particular example. 

  1. Active Learning Tutorial with Python

Written by a CS and EE student at IIT, India, this post is an in-depth tutorial on using active learning with Python. The tutorial is technical, explaining the code and its concepts through codes and steps. In addition, the post discusses various inputs, outputs, and the Python codes needed to apply active learning correctly.

  1. A Proactive Look at Active Learning Packages

Alexandre Abraham, a senior research scientist at Dataiku and a Ph D holder in computer science, has written an extensive tutorial on active learning packages on his Medium blog post. The blog post analyses the active learning packages available through a feature comparison, their covered approaches, and their coding aspects. There are three main packages and different methods that data scientists can leverage.


  1. Submodularity in Data Subset Selection and Active Learning

The paper in discussion is written by Kai Wei, an assistant professor at UCLA, Rishabh Iyer, an assistant professor at the University of Texas, and Jeff Bilmes, a professor at the University of Washington. Their paper studies the problem of selecting a subset of data to train a classifier and how individuals can apply the active learning framework to mitigate the issue. 

Online courses

  1. Machine Learning Data Lifecycle in Production

The DeepLearning.AI course in ML data lifecycle has a fourth module, tagged ‘Advanced Labeling, Augmentation and Data Preprocessing’, that focuses on semi-supervised learning, dataset labelling, and the role played by active learning within. The instructor, Robert Crowe, works at TensorFlow by Google and has multiple degrees in AI, ML and data science.

More Great AIM Stories

Avi Gopani
Avi Gopani is a technology journalist that seeks to analyse industry trends and developments from an interdisciplinary perspective at Analytics India Magazine. Her articles chronicle cultural, political and social stories that are curated with a focus on the evolving technologies of artificial intelligence and data analytics.

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