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The Only List of Top Machine Learning Newsletters You’ll Ever Need

Over the last several years, rapid advancements in machine learning (ML) have led to innumerable possibilities. Machine learning is a subfield of artificial intelligence. It deals with computer systems that can learn, adapt, and imitate human behavior. 

Science newsletters, such as the best machine learning newsletters, are great ways to explore written, audio, or video content on artificial intelligence (AI), academic news, and industry news. Below is a list of top machine learning newsletters and some lesser-known ones to help provide a well-rounded view of the industry and its topics.  

List of Top Machine Learning Newsletters

Newsletter Curator/Distributor Schedule Contents
AI Weekly David Lissmyr Weekly industry news, applied use cases, robotics, research and AI ethics literacy
Data Elixir Lon Riesberg Weekly Insights, latest code, ML tools and courses, projects and opinion pieces
Inside AI Rob May Daily Stories, practical articles and commentaries on ML hot topics like NLP, and news to readers
KDnuggets News Gregory Piatetsky-Shapiro Daily Tutorials, webinars, courses, new software, and job postings
Machine Learning Monthly Daniel Bourke Monthly Blog posts, usescases to solve world’s biggest problems, articles by experts, research papers, links to podcasts and videos, information on ML tools, and Bourke’s own work
Paper With Code Robert Stojnic, Ross Taylor, Marcin Kardas, Viktor Kerkez, Ludovic Viaud, Elvis, and Guillem Cucurull Bimonthly Trending ML papers with code, research developments, libraries, methods, and data sets
O’Reilly Data Newsletter O’Reilly Media
Weekly Latest trends, insights, tutorials, opinions, and case studies from industry experts
Talking Machines Katherine Gorman and Neil Lawrence Weekly News and opinions about machine learning systems, events, and job postings
The Machine Learning Engineer The Institute for Ethical AI and ML Weekly Articles, tutorials, blogs posts, insights on best practices, and ML tools
TWIML Sam Charrington Weekly Podcasts, science articles, favorite blogs and conferences relating to ML and AI.

The Biggest Machine Learning Newsletters, Explained 

AI Weekly

This is a free, weekly newsletter that features ML and AI resources and news for engineers working in machine learning. It is one of the most popular technical newsletters covering relevant articles on the latest AI tools and ML hardware. It also tackles ethical and regulatory questions surrounding the use of AI. 

Data Elixir

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This free, in-depth newsletter covers ML, data visualization, analytics, and strategy. Its curator, Lon Riesberg, is an ex-NASA data scientist and a big name in the data science community. Data Elixir is a highly respected newsletter, connecting readers to well-researched articles and relevant content on building specialized machine learning systems, skills, and expertise. 

Inside AI

This daily newsletter, curated by Rob May, is the closest to a real-time roundup. It covers the latest in AI, robotics, and neurotech. Inside AI features daily stories, helpful articles, and commentaries on artificial intelligence, and deep learning hot topics and news. In addition, it contains succinct news summaries of each article featured in the issue.  

KDnuggets News

KDnuggets News is curated by Gregory Piatetsky-Shapiro and acts as one of the leading newsletters on AI and the development of machine learning systems. This free newsletter comes out twice a month and contains a collection of stories featured on the KDnuggets blog and online news outlet. It also features tutorials, webinars, courses, upcoming events, and job postings.

Machine Learning Monthly

Daniel Bourke is an ML and deep learning expert who runs this free, monthly newsletter. The January 2022 theme for the newsletter was “building your own projects.” It featured Bourke’s own projects, blog posts from Google AI, use-cases for AI to solve world hunger, featured articles by Eugen Yan, thought-provoking essays like “Can AI help heartbreak?,” and information about ML tools like DAGsHub.

Paper With Code

This is a free and open resource for ML papers, code, datasets, methods, and evaluation tables released twice a month. For example, the most recent newsletter covered novel methods for improving OOD detection, a unified multimodal pertaining framework, a summary of how vision transformers work, and a section featuring new machine learning systems.

O’Reilly Data Newsletter 

O’Reilly Media’s weekly industry newsletter features AI news, industry insider insights, and exclusive deals and offers. It provides a collection of links to use-case stories and sites, and commentaries and perspectives on the featured news. In addition, this readable newsletter covers the latest trends, insights, tutorials, opinion pieces, and case studies from industry experts. 

Talking Machines

Talking Machines is a free, weekly newsletter that keeps its readers updated on the latest trends in the machine learning systems community. It contains ML podcasts, relevant news, major events, and job postings. In addition, this newsletter offers a window for conversations amongst industry experts on the latest trends in the development of machine learning. 

The Machine Learning Engineer 

The Machine Learning Engineer newsletter is a weekly digest run by a team that includes ML engineers, data scientists, industry experts, policy-makers, and professors in STEM, humanities, and social sciences. Its curated articles, tutorials, and machine learning blog posts include insights on best practices, tools, and techniques in ML explainability, reproducibility, model evaluation, and feature analysis.


TWIML features articles and relevant blog posts about the macro trends in machine learning. The newsletter also features weekly news articles about conferences or events related to ML and AI. Sam Charrington, an industry expert, is also curating a podcast called TWIMLAI. ML experts band together to share ideas with a broader AI and ML community, including researchers, data scientists, engineers, and business strategy experts. 

What Makes a Machine Learning Newsletter Popular? 

  • Original content. With the plethora of machine learning newsletters available, the originality of content is often what sets the popular ones apart. Original content pays off whether it uses humor, short summaries, or facilitates conversations with community experts. A weekly, bare-bones text newsletter will not be captivating enough to build a following.
  • A seamless graphical experience. If a newsletter has excellent content but is not easily readable, accessible, or seamless in design, it’s much less likely to become popular. Therefore a thorough visual inspection of the newsletter is required. When a smooth digital experience and unique content are paired together, the newsletter is bound to be popular. 
  • A voice of authority. The newsletters which provide opinions and commentaries that stem from experience in the ML field are usually the best-received. Such newsletters act as thought leaders in the ML field. Of course, if the curator is well-respected in the industry, so is the newsletter. 

Should I Look Beyond the Biggest Machine Learning Newsletters?

Yes, you should look beyond the biggest ML newsletters as they may not necessarily be the best. The biggest ML newsletters tend to cover a wide range of AI subjects, resulting in limited ML-focused content. Alternatively, smaller ML newsletters often focus on a specific niche, which allows you to quickly find information on exactly what you need.

3 Reasons to Check Out Less Popular Machine Learning Newsletters

  • Carefully curated content. The less popular newsletters are usually the ones that are run by one or two industry experts who cannot afford a whole media team. Such newsletters contain more insight than the popular ones.
  • An opportunity for a deeper dive. Less popular newsletters tend to provide in-depth knowledge. They serve as a window to the kind of news stories and works that industry experts are interested in. As an added bonus, knowledge of such facts can serve as an impressive talking point, or an interesting segway during job interviews. 
  • No rummaging through the newsletter. A less popular email newsletter will have less content when the editorial team is small. For example, it doesn’t spam the reader with offers or deals. Instead, it focuses on machine learning curiosities and delivering the most important news to your inbox. 

Are the Biggest Machine Learning Newsletters Necessarily Better?

No, the biggest ML newsletters are not necessarily better. If the content is not carefully curated and the focus is on quantity rather than the quality of knowledge, serious readers will look elsewhere. Focusing on the quality of content is the most important, whether the newsletter is big or small. 

Machine Learning Newsletters FAQ 

What is machine learning in simple terms?

Machine learning refers to machines imitating intelligent human behavior. There are four types of machine learning systems including supervised, semi-supervised, unsupervised, and reinforcement. You can use online resources to learn more about machine learning.

What are some examples of machine learning?

Examples of machine learning are image and speech recognition software, medical diagnosis, automated trading strategies, extraction of information, and predictive texts. In the near future, US institutes plan to use ML and AI-related technologies to improve the criminal justice system, traffic issues, and cyber defense. 

What are the steps of building a machine learning model? 

The seven steps to building a machine learning model include collecting data from reliable sources, cleaning and removing unwanted data, picking a relevant ML model, training that model, evaluating it, tuning parameters to look for needed improvement, and testing it on unseen data. 

What is the difference between artificial intelligence and machine learning?

While machine learning (ML) and artificial intelligence (AI) are often used synonymously, there is a difference between them. ML is just a subfield of AI. Other major subfields of AI also include neural networks, evolutionary computation, vision, robotics, expert system, speech processing, natural language processing, and planning. 

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