Why subscribe to Data Machina?

Data Machina brings you a highly curated selection of the best in Machine Learning, AI, Data Science, and Data Engineering every week, 52 weeks per year.

Loaded with useful, unique, and interesting content, Data Machina is read by thousands of ML professionals and researchers around the world.

Data Machina is published in a minimalistic, easy-to-read format, with pure, simple text, and structured in clearly marked sections so you can scan them quickly without being disturbed by ads, banners, icons, images or other annoying stuff.

You can subscribe to Data Machine in 2 ways:

  1. FREE: Receive a short version of Data Machina every two weeks. 

  2. PAID: Receive the full version of Data Machina, featuring new content and insight, every week of the year.

    To receive the paid version of Data Machina, you will need to confirm your paid subscription with your current subscriber email.

    If your university or company team would like to purchase group subscriptions (minimum five people) at a discounted rate, please contact me at datamachina@substack.com.

    The PAID subscription includes 52 weekly newsletters per year with:

    • Commentary of the week on important, new or cool topics

    • The widely popular 10-Link-o-Troned - A highly curated selection of the most interesting, unique and new ML/AI topics of the week

    • Five sections with hands-on, practical code and Machine Learning projects in Python, R, Julia, Clojure & Scala

    • A section on Distributed Systems & Data Engineering

    • A section on Deep Learning and other types of learning like Reinforcement Learning, Unsupervised Learning, Transfer Learning and many more

    • A section with useful, cool Datasets for Machine Learning

    • A curated list of relevant, important, or novel Research Papers

    • A section highlighting new, interesting, or useful Algorithms

    • A section with amazing, curious, or innovative Data Visualisations

    • A section covering the latest in Robotics and Machine Intelligence

    • A list of AI/ML Startups that are worth mentioning or notable

    • A section on IoT, Embedded & Intelligent Devices, Edge Computing

    • Highly recommended Long Reads

    • A section with Free Interesting ML e-Books

    • A section with Free, Useful ML Courses and Workshop

    • Coming soon - Interviews with ML colleagues

    • Coming soon - Guest posts from ML colleagues

Who reads Data Machina? Join thousands of colleagues

Data Machina is read by more than 8,000 professionals and researchers every week. Here’s who reads Data Machina; join a global community of:

  • ML Professionals who work in world-class teams at Google, Twitter, Spotify, Apple, Microsoft, Amazon, Intel…

  • ML Academics and Researchers from world-leading universities like MIT, Stanford, UC Berkeley, Yale, Princeton, Duke, Cornell, Columbia, UCL, Imperial College…

  • ML Geeks, Hackers and S/W Engineers who work in startups in London, New York, San Francisco, Shanghai, Beijing, Redmond, Berlin, Frankfurt, Barcelona, Madrid, Amsterdam, Zurich, Sydney …

  • ML Analysts from top global consultancies like McKinsey, Boston Consulting Group, Bain, PwC, Deloitte, Accenture, Ernst & Young, KPMG…

Who curates Data Machina?

Hello! I’m alg01 a human algorithm that works ingesting hundreds of information feeds in the middle of the night, 7 nights a week.

I have a human neural network that filters and selects only the most interesting, useful, unique and novel content. I then publish all that cool content into a nice structured package so that you can read it smoothly.

My human classification algorithm detects hype, bs, worthless content, marketing propaganda, and then automatically sends all that shite content to the e-bin so that you’re not disturbed and your time is not wasted.

I’m also the algorithmic alter-ego of Carlos.

During the day, Carlos advises corporate technocrats in large enterprises on how the heck to use Machine Learning and Data Engineering to do stuff that delivers value or at least some value; which -you guessed well- is a bit complicated, because of humans… you know…