Guide to Interpretable Machine Learning by @MatthewPStewart via @TDataScience

Techniques to dispel the black box myth of deep learning.

This is great and very detailed so put aside some time to read it as well as giving applause on the article.

WEBINAR: Managing Risk in Decentralized Networks with Time Series 24 March 2020

Data Science Central Webinar Series Event

Managing Risk in Decentralized Networks with Time Series Join us for this latest DSC Webinar on March 24th, 2020 A distributed network is key to providing storage at lower costs than other cloud providers. By equipping customers with fast, secure and fully distributed storage, users no longer need to manage infrastructure. This platform enables applications to store and share end-to-end encrypted data across a distributed network. Discover how a time series database is a key component to their service. In this latest Data Science Central webinar, John Gleeson and Ben Sirb will dive into: The definition of a cloud object storage network The advantages of using open source software in a modern tech stack How high-volume, real-time telemetry data is used to inform key business decisions A forecast predicting the impact of node churn on object health Speakers: John Gleeson, VP of Operations -- Storj Labs International, SEZC Benjamin Sirb, PhD, Sr. Data Scientist -- Storj Labs International, SEZC Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Managing Risk in Decentralized Networks with Time Series Date:  Tuesday, March 24th, 2020 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now   After registering you will receive a confirmation email containing information about joining the Webinar.

WEBINAR: The Human Impact of Data Literacy: 5 Steps to Driving Data Leadership in 2020 - 25 March 2020

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WEBINAR The Human Impact of Data Literacy: 5 Steps to Driving Data Leadership in 2020 Wednesday, 25 March 2020 10:00 UK / 11:00 CET REGISTER   Your next big opportunity: Data literacy for all Chances are, you're already striving to get the most possible value from your data. But if you're not addressing the human component, you're missing a big opportunity. Only 21% of workers are confident in their data literacy skills.* That leaves 79% who could use some help. Upskilling your entire workforce – and not just your specialists – can exponentially increase the value of your data. Join this webinar to discover the remarkable impact of data literacy, including: The size of the opportunity – up to $500M* The 3 main barriers to becoming data-driven 5 steps for building a data-informed workforce Practical advice from featured analyst Forrester plus industry leaders at Nemours and Qlik Register now to reserve your spot. (*) - The Data Literacy Index, commissioned by Qlik and conducted by IHS Markit, PSB Research, and academics from the Wharton School at UPenn. Wednesday, 25 March 2020 at 10:00 UK / 11:00 CET REGISTER   Webinar Speakers Martha Bennett VP, Principal Analyst, Forrester   Rishi Muchhala Manager of Enterprise Intelligence, Nemours Children's Health System   Jordan Morrow Global Head of Data Literacy, Qlik REGISTER HERE

The Most Useful ML Tools 2020 by @ian_xxiao via @TDataScience

5 sets of tools every lazy full-stack data scientist should use

This is a great list of tools and should give you a list of tools to try. Give Ian some applause on Medium and a follow.

The Ultimate Beginner’s Guide to Data Scraping, Cleaning, and Visualisation by @annebonnerdata via @TDataScience

How to take your model from unremarkable to amazing simply by cleaning and preprocessing your data.

Anne is right - if you get the underlying data right, the results and process to get to there is so much easier.  That is NOT to say you do something to skew the results, just that you make sure that it is not a case of garbage in/garbage ou.

WEBINAR: Machine Learning, Explainable AI, and Data Visualization 19 March 2020

Data Science Central Webinar Series Event

Machine Learning, Explainable AI, and Data Visualization Join us for this latest DSC Webinar on March 19th, 2020 The promise of artificial intelligence is that machines will help humans make better decisions. But we need to understand what machines are doing, to avoid mistakes and to understand our data. As organizations become more reliant on AI and machine learning models, how can humans be sure they are receiving trustworthy answers to the right questions? In this latest Data Science Central webinar, we will be discussing some general trends in AI before digging into what's been happening in explainable AI research, what are some of the readily available tools, and what’s coming next. We will also address integrating AI machine learning capabilities into Tableau, and that will give us an opportunity to deep dive into the recent developments at Tableau regarding the topic of XAI. Speaker: Richard Tibbetts, Principal Product Manager for AI -- Tableau Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Machine Learning, Explainable AI, and Data Visualization Date:  Thursday, March 19th, 2020 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

What AI still can’t do by Brian Bergstein via @techreview

Humans aren’t very good at understanding causation either.

I agree with Brian that we need to change our current AI thinking and combine a lot more sources of information using neural networks in order to get much better results.

Quantifying Independently Reproducible Machine Learning by @EdwardRaffML via @gradientpub

Through a “combination of masochism and stubbornness,” Edward Raff, chief scientist at Booz Allen, spent eight years trying to reproduce the results from 255 papers (with only 162 successful reproductions). This paper distils the 26 key features of reproducibility.

It's really important, that if you want others to believe your results, that they can analyse and reproduce them. It's a founding principle of all medical research and analysis that is presented to peers for review and that they are able to reproduce it. Yes, this paper is aimed at machine language, but I believe you can apply some of these for anything. Read it, bookmark it, and keep a copy on your noticeboard.

Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms by/via @kdnuggets

The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.

Very interesting. Might give you some suggestions on what to look at and learn next?

15 Top-Paying IT Certifications for 2019 by/via @GlobalKnowledge

According to consulting firm Global Knowledge, those with one of the top 15 tech certifications reported average salaries over $100,000.

Definitely something to read and use to enhance your career.