In recent years, rapid advances in machine learning (ML) have opened up countless possibilities. Machine learning is a subfield of artificial intelligence. It is about computer systems that can learn, adapt and imitate human behaviour.
Scientific newsletters, like the best machine learning newsletters, are great ways to explore written, audio or video content about artificial intelligence (AI), academic news, and industry news. Below is a list of the best machine learning newsletters and some lesser known ones to give you a good idea of the industry and its topics.
List of the best machine learning newsletters
|AI Weekly||David Lissmyr||Weekly||Industry news, applied use cases, robotics, research and AI ethics skills|
|Dates Elixir||Lon Riesberg||Weekly||Insights, latest code, ML tools and courses, projects and opinion pieces|
|Within AI||Rob May||Daily||Stories, hands-on articles and commentary on current ML topics such as NLP, and news for readers|
|KDnuggets News||Gregory Piatetsky-Shapiroz||Daily||Tutorials, webinars, courses, new software and vacancies|
|Machine Learning Monthly||Daniel Bourke||Monthly||Blog posts, use cases to solve the world’s biggest problems, expert articles, research papers, links to podcasts and videos, information about 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 datasets|
|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 vacancies|
|The machine learning engineer||The Institute for Ethical AI and ML||Weekly||Articles, tutorials, blog posts, best practice insights and ML tools|
|TWIML||Sam Charrington||Weekly||Podcasts, scientific articles, favorite blogs and conferences related to ML and AI.|
The biggest machine learning newsletters, explained
This is a free, weekly newsletter with ML and AI resources and news for engineers working on machine learning. It is one of the most popular tech newsletters with relevant articles on the latest AI tools and ML hardware. It also addresses ethical and regulatory questions surrounding the use of AI.
This free, in-depth newsletter covers ML, data visualization, analytics, and strategy. The 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 that connects readers with well-researched articles and relevant content on building specialized machine learning systems, skills and expertise.
This daily newsletter, compiled by Rob May, is the closest we can get to a real-time overview. It covers the latest developments in AI, robotics and neurotech. Inside AI provides daily stories, helpful articles and commentary on artificial intelligence, and in-depth learning topics and news. In addition, it includes succinct news summaries of each article in the issue.
KDnuggets News is curated by Gregory Piatetsky-Shapiro and serves as one of the leading newsletters on AI and machine learning system development. This free newsletter is published twice a month and features a collection of stories on the KDnuggets blog and online newscast. It also includes 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 theme of the January 2022 newsletter was ‘Build your own projects’. It featured Bourke’s own projects, Google AI blog posts, use cases for AI to solve world hunger, articles by Eugen Yan, thought-provoking essays such as “Can AI help heartbreak?” and information about ML tools such as DAGsHub.
Paper with code
This is a free and open source for ML papers, code, datasets, methods and evaluation tables released twice a month. For example, the most recent newsletter featured new methods for improving OOD detection, a unified multimodal framework, a summary of how vision transformers work, and a section on 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 comments and perspectives on the featured news. In addition, this readable newsletter contains the latest trends, insights, tutorials, opinion pieces and case studies from industry experts.
Talking Machines is a free, weekly newsletter that keeps readers informed about the latest trends in the machine learning systems community. It features ML podcasts, relevant news, key events and job openings. In addition, this newsletter provides a window for conversations between industry experts about the latest trends in machine learning development.
The machine learning engineer
The Machine Learning Engineer Newsletter is a weekly digest managed by a team that includes ML engineers, data scientists, industry experts, policy makers, and professors in STEM, humanities, and social sciences. The curated articles, tutorials, and machine learning blog posts provide insights into best practices, tools, and techniques in ML explainability, reproducibility, model evaluation, and feature analysis.
TWIML contains articles and relevant blog posts about the macro trends in machine learning. The newsletter also includes weekly news articles about conferences or events related to ML and AI. Sam Charrington, an industry expert, also curates a podcast called TWIMLAI. ML experts collaborate to share ideas with a wider 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’s humor, short summaries, or facilitating conversations with community experts. A weekly, bare-bones text newsletter isn’t going to be compelling enough to build a following.
- A seamless graphics experience. If a newsletter has excellent content, but is not easily readable, accessible, or seamless in design, it is 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 linked together, the newsletter will undoubtedly become popular.
- A voice of authority. The newsletters with opinions and comments that come from experience in the field of ML are usually the best received. Such newsletters act as opinion leaders on ML. 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 because they don’t necessarily have to be the best. The largest ML newsletters tend to cover a wide variety of AI topics, resulting in limited ML-focused content. Alternatively, smaller ML newsletters often target a specific niche, allowing you to quickly find information about exactly what you need.
3 reasons to watch less popular Machine Learning newsletters
- Carefully curated content. The less popular newsletters are usually those from one or two industry experts who can’t afford an entire media team. Such newsletters contain more insight than the popular ones.
- An opportunity for a deeper dive† Less popular newsletters often provide in-depth knowledge. They serve as a window into the kinds of news stories and works that industry experts are interested in. As an added bonus, knowledge of such facts can serve as an impressive conversation piece or an interesting segway during job interviews.
- No messing around in the newsletter. A less popular email newsletter will have less content if the editors are 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 emphasis is on quantity rather than quality of knowledge, serious readers will look elsewhere. Focus on the quality of the content is the most important, whether the newsletter is large or small.
Machine Learning Newsletter FAQ
Machine learning refers to machines that imitate 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.
Examples of machine learning include image and speech recognition software, medical diagnosis, automated trading strategies, information extraction, and predictive texts. In the near future, US institutions plan to use ML and AI-related technologies to improve the criminal justice system, traffic problems and cyber defense.
The seven steps to building a machine learning model include collecting data from trusted sources, cleaning and removing unwanted data, choosing a relevant ML model, training that model, evaluating, tuning parameters to search for necessary improvements and test for invisible data.
Although machine learning (ML) and artificial intelligence (AI) are often used synonymously, there is a difference between the two. ML is just a subfield of AI. Other important subfields of AI also include neural networks, evolutionary computation, vision, robotics, expert system, speech processing, natural language processing, and planning.
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