Machine learning certifications?
Well the word of the week seems to be #Metaverse and that in and of itself is interesting. Back in 1992 a science fiction writer named Neal Stephenson wrote a book called Snow Crash and it used the word and described how it would work in practice.[1] A lot of people are probably picking up that book or maybe downloading the audiobook. What does that have to do with machine learning certifications? Obviously, I spent a chunk of my morning trying to find one and most of it seemed to be hyperbole or sarcasm. It might require a little bit more digging in academic tomes to figure out a well struck walk from machine learning to the Metaverse. I’m pretty certain that machine learning will be used in the Metaverse, but we will see what happens shortly on this one as people appear to be actively trying to build it.
From that bit of whimsy to something more practical:
People are always looking for practical machine learning experience. It’s all about the applied machine learning efforts on a resume and the ability to demonstrate real and practical skills. Certifications have and will probably remain a keyword or a method for people to talk about what they know and a demonstration of mastered skills. Within any stringent interview the questions and demonstration of skills will probably separate the paper learning from the applied learning very quickly. You have to be able to demonstrate that you can apply the knowledge to the problems in question or even the best certifications are going to end up just looking nice printed out on the refrigerator. It’s the demonstration of skills that makes certificates become more than just a line on a resume. That is one of the reasons that I have really tried to put some code out on GitHub and to demonstrate and work actively in the space.
The bottom line on machine learning certifications is that they are a great way to demonstrate that you were learning and passing labs. Practical experience needs to be mixed in with the certifications so if you can use the demonstration of skills to land a job or actively contribute to projects on GitHub as part of the community you can work toward a trajectory of employment. The moral of the story here is that a stack of machine learning certificates alone are not going to be enough without some type of demonstration of your skills. A lot of interviews these days tend to include some type of exercise or challenge to prove out the skills. I really enjoyed a lot of the machine learning certificates that I have collected over the years. The labs have gotten so much better in simulating what you need to do in the actual environments. That type of practice really made machine learning come alive for me in a guided sort of way.
The folks over at Google Cloud just launched another specialization “Hands-on Machine Learning with Google Cloud Labs Specialization”
https://www.coursera.org/specializations/hands-on-machine-learning
Links and thoughts:
This week of Machine Learning Street talk was “#57 - Prof. Melanie Mitchell - Why AI is harder than we think”
Paper: Why AI is Harder Than We Think https://arxiv.org/abs/2104.12871
Go check out the Substack post this week from Rob May “Data Traction: How To Evaluate AI Companies With Little or No Revenue - Part 1: Beyond Traditional Metrics”
This week I watched Linus and Luke on the WAN show... “Dell Gaming PCs Got Banned - DON'T PANIC - WAN Show July 30, 2021” for the most part I watch a good chunk of this show every Saturday morning during the course of putting together these Substack posts.
The folks over at Microsoft Developer have been doing a pretty decent AI Show and I watched the episode this week “AI Show Live | Azure Percept – AI IoT Edge Made Easy with special guest-host Cassie Breviu - Part 1”
Top 5 Tweets of the week:
Footnotes:
[1] Here are some links from 2017 and 2019 to show that this has been brewing for a bit of time as an idea, but now people are trying to build it https://www.vanityfair.com/news/2017/06/neal-stephenson-metaverse-snow-crash-silicon-valley-virtual-reality or https://venturebeat.com/2019/12/21/snow-crash-inspired-the-metaverse-and-now-its-becoming-an-hbo-show/
What’s next for The Lindahl Letter?
Week 29: Machine learning feature selection
Week 30: Integrations and your ML layer
Week 31: Edge ML integrations
Week 32: Federating your ML models
Week 33: Where are AI investments coming from?
I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed reading this content, then please take a moment and share it with a friend.