The world of machine learning is less foggy and still intimidating.

Week one is now complete. I have not even contemplated my first assignment. I have to admit my confidence is still low. However, several things are clearer to me now. One of reason for that is the prereading we were given prior to the class. We had five short papers and two videos to read (total of about an hour). The papers dispelled some common misconceptions about Artificial Intelligence and gave us a great deal of definition. Understanding the hierarchy of data science, artificial intelligence, and machine learning has helped me conceptualize the foundation of becoming a machine learning practitioner.

One thing I think our cohort had in common coming into the machine learning course was a mix of uncertainty and lack of confidence in our ability. The cohort has a broad geographic dispersion and professional experience. Our first class started by setting up the basic concepts of machine learning, and for those a little more grounded in the concept, we even worked through the Brain Toy machine learning operating system (MLOS).

I have to admit I was a little lost with some of the steps, but with access to one-on-one coaching for my first assignment I do not foresee this being a barrier I cannot overcome. That being said, a big challenge I think everyone will need to overcome is our ability to couch machine learning into our workplace. Seeing machine learning in the context of the examples is helpful, but converting that into your own industry and your own real world setting is the gap we all must bridge.

Bridging this gap is a challenge that can be overcome by having the expertise available, and so far I have every confidence in the course leadership. We have been given some basic data sets that will help us learn to use the MLOS, and it is hands on. That is a good thing, but again, I am not an “Excel guru”, and I am not a “Tech guy”.

To be fully transparent I am not there yet. I still do not get machine learning as a practitioner. I will say that I now understand Machine learning as an informed bystander. I understand Artificial Intelligence and Machine Learning as a better-informed layman. Considering I have spent a total of five hours on the subject, I would say that is not too shabby. I am still somewhat terrified and highly unqualified. I am still not convinced I really will ‘get it’, but I am far from giving up.

John image for bio Supply Chain Canada

TOM MCCAFFERY

President & CEO, Alberta Institute

TOM’S CONFIDENCE METER

Leave a Reply