Mode Analytics Learn SQL Learn Python Data Viz Analytics Dispatch Forum

Machine Learning Articles

Machine learning, deep learning, artificial intelligence... The science of getting machines to perform actions without explicitly programming them to do so can be intimidating for the uninitiated. These machine learning articles aim to unpack the black box for beginners, with introductions to overall concepts and tutorials for training a model of their own.

J.P. Morgan’s massive guide to machine learning and big data jobs in finance

Get the key takeaways from this 280-page report, including essential data analysis packages, hiring tips, and which machine learning techniques to apply to which problems. - efinancialcareers

“Many enterprise ‘AI products’ and ‘machine intelligence’ products built today have limited appeal or impact”

One investor’s self-described “unpopular” opinion - Sarah Guo


Save this hashtag for the moments when you need to jog your memory on some basic concepts. - Chris Albon

Is Your Organization Ready for ML?

Don’t make this mistake: “[M]any organizations rush to hire ML experts without laying the proper foundation to ensure their success, including creating proper database architecture, building out essential data science technology, establishing data governance, and instilling data-driven decision-making throughout the organization.” - RE•WORK

Machine Learning for Product Managers

A brilliant, non-technical read for anyone who designs, supports, manages, or plans for products that use machine learning. - Hacker Noon

Distill: An Interactive, Visual Journal for Machine Learning Research

This new online publication is bringing academic journals into the 21st century: “A Distill article… isn’t just a paper. It’s an interactive medium that lets users – 'readers' is no longer sufficient – work directly with machine learning models.” - Y Combinator

Tips & Tricks for Feature Engineering / Applied Machine Learning

One commenter put it best: 'Probably the best feature engineering slides I have found [on] the internet.' Need we say more? - HJ van Veen

Learning about Machine Learning with an Earthquake Example

How well can we predict whether or not someone is prepared for an earthquake? - Simply Statistics

Fake News Challenge

This grassroots effort is inviting teams to harness AI technologies to help human fact checkers identify hoaxes and deliberate misinformation in news stories. The top three teams get a cash prize, so grab a couple of friends and check out the training dataset. - Fake News Challenge

How Fitbit’s data science team scales machine learning

Workout regimens need to be tailored to each individual. Directional correctness isn’t enough. Fitbit’s head of data science shares how his team builds a model for every user to increase motivation and prevent injuries. - Mixpanel

Machine Learning Videos

More of a visual learner? Here’s a repository of recorded talks at machine learning conferences, workshops, seminars, and more. - Dustin Tran

What is artificial intelligence? A three part definition

“As soon as it works, no one calls it AI anymore.” - Simply Statistics


You could be a poet, and not know it. Feed the works of your favorite author through this new Python library to generate as many lines of verse as you want. - Anthony Federico

What I Learned Implementing a Classifier from Scratch in Python

With libraries like scikit-learn, it’s easy to run an algorithm on some data and automagically get an answer—without understanding exactly how you arrived there. Prepare to unpack the black box. - Jean-Nicholas Hould

What’s the state of the job market in data science and machine learning?

“Th[e] proliferation of courses, resources, books and startups would hint that machine learning is becoming more and more accessible to the average programmer and that the market is on track to getting saturated quickly. Is this the current trend?” - Hacker News

20 Weird & Wonderful Datasets for Machine Learning

Getting your hands on a robust dataset is the hardest part of machine learning. Finding interesting datasets is tougher still. From UFO sightings to beautiful Flickr photos, you’re sure to find something to train your model. - Oliver Cameron

Deep-Fried Data

Opening your data can lead to unpredictable benefits, but requires being open to unexpected uses of your data. - Idle Words

Deep Learning Isn’t a Dangerous Magic Genie. It’s Just Math

This essay is a godsend for those of us who have trouble understanding or explaining what exactly deep learning is. - WIRED

Boosting Sales With Machine Learning

One developer shares how his team used natural language processing and machine learning in Python to pre-qualify sales leads so reps don’t have to spend hours doing it manually. - Xeneta

Hybrid Intelligence: How Artificial Assistants Work

When humans and machines work together, they accomplish a lot more than either could on their own. This is known as hybrid intelligence—a pretty intimidating term for those unfamiliar with machine learning. Here’s a breakdown. - Clare Corthell

The real prerequisite for machine learning isn’t math, it’s data analysis

Machine learning amateurs, take heart. Proficiency with high level math may be essential for machine learning theory. But with out-of-the-box tools like R’s gmodels package or Python’s scikit-learn library, you don’t need to know linear algebra or calculus to build a successful predictive model. You do, however, need to know your way around a dataset. - Sharp Sight Labs

How Kalman Filters Work, Part 1

This article unpacks different filtering algorithms in an incredibly intuitive way. It’s a long read, but you’ll come away having learned a ton (did you know that NASA used Kalman filters to help Apollo spacecraft navigate to the moon?). - An Uncommon Lab

Lift analysis - A data scientist’s secret weapon

Learn how to spot flaws in machine learning models with lift analysis (and why you should add it to your list of evaluation metrics). - Andy Goldschmidt

Microsoft’s Tay is an Example of Bad Design

0r Why Interaction Design Matters, and so does QA-ing. - Caroline Sinders

Here's How We Prevent The Next Racist Chatbot is the consequence of poor training - Popular Science

Explained Visually

This website is an incredible collection of interactive visualizations aimed at making tricky concepts like Markov chains and regression easy to understand. Schedule a few hours to explore this one—you’re gonna need them. - Explained Visually

Why Microsoft Accidentally Unleashed a Neo-Nazi Sexbot

It’s not surprising that Microsoft’s chatbot spewed racist invective, but here’s how it could have been avoided. - MIT Technology Review

We Now Have Algorithms To Predict Police Misconduct

You’ve probably heard of predictive policing, but what about predictive policing for the police? One police department teamed up with researchers to test an algorithm that detects troublesome behavior of officers early on. - FiveThirtyEight

Are Your Predictive Models like Broken Clocks?

How can you ensure you’ve picked the “right model” for a very big and very complex dataset? - Rocket-Powered Data Science

Startups Aim to Exploit a Deep-Learning Skills Gap

What do you do when every company wants to build a deep-learning network, but the experts are in short supply? Launch a product, of course. Some startups have created computer chips and software libraries that can accelerate algorithm training, all without having to hire an experienced team of deep-learning experts. - MIT Technology Review

Georgia Tech Researchers Demonstrate How the Brain Can Handle So Much Data

Random projection is frequently used in machine learning to make sense of big, diverse data. It turns out this method could be one of the ways that humans learn, too. - Georgia Tech

The current state of machine intelligence 2.0

These days, it feels like every other article in our newsfeeds is touting the potential of machine intelligence. This article cuts through the hype and presents this year’s major accomplishments in two categories—“(1) the emergence of autonomous systems in both the physical and virtual world and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.” - O'Reilly