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They were more prepared to tackle the day. They trusted me and felt more comfortable asking questions and taking risks. They were reaching out for help and clarification more than ever before. The discipline problems I had been having started to decrease, and honestly, the school day was so much more enjoyable for all of us. We were building trust and friendships, and the students knew that I was on their side and willing to help.

Parents would reach out more, and I started to hear that kids really enjoyed being in my class. I never abandoned my work on curriculum, data, differentiation, and all of the other things that are essential in a classroom, but I did shift my priorities. New teachers often ask me where I find the time to build relationships in the classroom.

I always tell them that it should be the first thing they build into their plans. Prioritize it at the beginning of the year and continue it each day. Just as one allocates time to teaching procedures, an equal amount of time should go into building community and getting to know students. One of my favorite classroom games is Who Is It? Each student writes two to three unique things about themselves on a Post-it note with their name.

There are two gaps in putting machine learning into practice. The gap of going from course work to project work and the gap going from models in a notebook to models in production model deployment.

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An internet search for machine learning courses returns a plethora of results. I used many of them to create my own AI Masters Degree. But even after completing many of the best ones, when I started as a machine learning engineer, my skills were built on the structured backbone of courses.

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  • I lacked specific knowledge. How to question data, what to explore versus what to exploit. I was lucky to be around the best talent in Australia.

    My First Year

    But I was also willing to learn and willing to be wrong. But I did notice a trend. Machine learning engineering and software engineering are merging. With services like Seldon , Kubeflow and Kubernetes soon machine learning will be another part of the stack. Judging by the view counts on recent talks at Cloud Native events, not many people outside of large companies know how to do this. We had a rule. Things was a loose term, meaning things in the world of machine learning. This proved invaluable more than once.

    If your business advantage is being the best at what you do now, future business depends on you continuing to being the best at what you do. This means constantly learning. This is a rough metric. In other words, out of thousands of submissions per year, you may have 10 groundbreaking papers. And out of those 10 groundbreaking papers, 5 might come from the same institute or person. Better to get a solid foundation of the basic principles and apply them.

    These are the ones which have stood the test of time.

    The original breakthroughs the new breakthroughs refer to. But then comes the exploration versus exploitation problem. You can deal with the exploration versus exploitation problem by being your own biggest sceptic. The exploration versus exploitation problem is the dilemma between trying something new and reapplying what already works.

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    Toy problems work. Especially to help understand a new concept. Build something small. It could be a small subset of your data or unrelated dataset. Being in a small team, the trick was to get something working, then iterate, fast. Ron taught me this one. Instead, talk it out in language with a teammate.

    This ties back into the point about machine learning engineering merging with software engineering. Unless your data problem is very specific, many of the major ones are quite similar, classification, regression, time series predictions, recommendations. Knowing the basic principles of data science and machine learning is still required. But knowing how to apply them to your problem is even more valuable. For the client problems I worked on, we were all code first. All the machine learning and data science code was Python.

    Knowing at a minimum matrix manipulation, some linear algebra and calculus, particularly the chain rule is good enough to be a practitioner.

    Reflecting on My First Year at University

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    Join Group settings More. Gesika Weis 14 hrs. Han Zhang 11 hrs. Ben Downie 9 hrs. Jessy Sidhu shared a link. Looking for a way to be more involved on campus? Support your peers, help them gain knowledge and skills to succeed in their academic careers and beyond! SHAPE has volunteer groups focused on healt Nerissa Kassis September 20 at AM.