Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
A University Curriculum Supplement to Teach a Business Framework for ML Deployment
    In 2023, as a visiting analytics professor...
The AI Playbook: Providing Important Reminders to Data Professionals
 Originally published in DATAVERSITY. This article reviews the new...
Decode the Algorithm: Navigate the World of Machine Learning in Business with ‘The AI ​​Playbook’
  This article reviews the new book, The AI Playbook, by...
To Deploy Machine Learning, You Must Manage Operational Change—Here Is How UPS Got It Right
 Originally published in Harvard Data Science Review. For more...

data science

Keeping Data Inclusivity Without Diluting your Results

 Originally published in WeAllCount.com, January 17, 2020 Let’s say you are surveying 100 people out of 10,000. You want to analyze the data from your sample of 100 to get answers about the likely behaviors and preferences of the overall 10,000 person population. Part of your project focuses on equity among sexual orientations. You don’t

The ML Times Is Growing – A Letter from the New Editor in Chief

  Dear Reader, As of the beginning of January 2020, it’s my great pleasure to join The Machine Learning Times as editor in chief! I’ve taken over the main editorial duties from Eric Siegel, who founded the...

The 4 Hottest Trends in Data Science for 2020

 Originally published in Towards Data Science, January 8, 2020 2019 was a big year for all of Data Science. Companies all over the world across a wide variety of industries have been going through what people are...

How to Perfect Your Data Science Resume in 3 Easy Steps

 Breaking into the world of Data Science can be tricky, but writing a killer resume gives you a better chance of landing a job in this highly competitive field. There are a few simple steps you can...

Three Common Mistakes That Can Derail Your Team’s Predictive Analytics Efforts

  Originally published by Harvard Business Review With today’s high demand for data scientists and the high salaries that they command, it’s often not practical for companies to keep them on staff.  Instead, many organizations work to...

Why Your Analytics Must Ask the Data “Good” Questions — Ones that Reduce Data

 The problem of monetizing Big Data, or improving its usefulness in decision-making, stands out in every forward-looking organization today. To solve it, as everyone knows by now, it is not enough to store or manage it. We...

The Art of Data Science

 With much of the latest discussion focused on the latest techniques in machine learning  and in particular deep learning, the significant benefits of machine learning and deep learning are now a public reality. Yet, machine learning in...

Mind Your Own Text: Public Data for Political Insights

 Who is your favorite president? Can Data Science aid in evaluating presidents and their policies? From the suburbs of Washington, D.C., our research group at George Mason University (Abhishek Kamath, Suyameendra Wadki, and Abhishek Madhusudhan) have developed...

Machine Learning Tip: Nested Cross Validation – When (Simple) Cross Validation Isn’t Enough

 Several scientific disciplines have been rocked by a crisis of reproducibility in recent years . Not long ago, Bayer researchers found that they were only able to replicate 25% of the important pharmaceutical papers they examined , and an...

Automation and Its Impact on Predictive Analytics – The Increasing Importance of the Hybrid-Part 3

 In my last article, I discussed the increasing impact of automation and its actual impact in creating the analytical file. As any data scientist knows, this component or stage of the data science process can typically represent...

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