4 lessons machine learning taught me

I remember sitting in a physical chemistry class in graduate school, watching a professor write on a series of chalkboards. This went on for more than 30 minutes. They were showing the class how to solve one problem. As I stared, it dawned on me that not one of the five chalk boards had a single number on it, just scribbles of various letters and symbols. Upon completion, the professor dropped his chalk into the tray and exclaimed, “See, it’s easy!” It was at this moment that a great life lesson dawned on me. Everything’s easy, if you know what you’re doing. 

I felt that way again later in life as I tried to fumble my way through various courses, videos, and blog articles trying to piece together how to do machine learning. Or maybe more accurately, how to start piecing together how to do machine learning. Searching for any length of time to find basic concepts explained without chalkboards of answers, super complicated statistical modeling, pages of Python scripts, or even the dreaded combination of several of those things … it can leave you feeling frustrated and ready to quit. 

These life lessons have helped to shape my career and how I teach others. So I’d like to share with you some of the lessons I’ve learned, and how you can apply them to take gradual steps towards the deep end of machine learning. 

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Seriously. You don’t need to be a master programmer or have a masters in statistics to start exploring machine learning. Jump in and take a few absolute beginner courses to get some of the basic concepts down. My new course, Introduction to Machine Learning Operations (MLOps) in Azure, is designed for absolute beginners, and uses no code or mathematical formulas to teach you the basics. There are a couple of different courses, like Introduction to Machine Learning, on A Cloud Guru that you can take to give you a good foundation. 

No matter which course you take, I recommend that it is a course, and that it is a basic course. Learning the foundations will make it much easier when you start layering in more complex concepts later. 

… and that’s okay! When you take a course, I recommend you keep a notepad with you. Jot down any questions you have and keep moving forward. You will learn quite a lot that way. Most importantly, you won’t spend hours or days diving down a rabbit hole trying to understand something that might not be all that important to your journey. 

Once you’ve completed a course and you have your notepad full of questions you can start hunting down the answers. Or you could jump in and take another course – or even the same course again! If you do, your knowledge will keep deepening, and before you know it you’ll be able to start answering some of the questions on your notepad without even needing additional research. 

In my Intro to MLOps in Azure course, one of the items I highlight is a pyramid of knowledge. It contains four layers: core, cloud, data engineering, and machine learning. Each of these layers holds concepts that will make you a better machine learning engineer or data scientist. 

As you start to get the basics down, layer in some of these additional concepts, like a Python course or a few machine learning models. By gradually introducing additional concepts, you won’t be so overwhelmed by complexity, but will instead enhance your learning at a manageable pace. 

Oh come on! You didn’t think I’d make it all the way through a blog post without mentioning hands-on labs, did you? It’s true though, hands-on labs are a FANTASTIC way to follow up your introductory course. By actually building machine learning models, you’ll start to understand how they work. This will make deeper dives much easier. You can also take a look at Microsoft docs or sample data sets in Azure Machine Learning to keep exploring. 

The most important takeaway I want to share comes from Desmond Tutu. He wisely once said, “there is only one way to eat an elephant: a bite at a time.” So start small. Start fun. Take an absolute beginner course. Do a hands-on lab. Don’t get bogged down in the hard stuff. 

Finally, repetition is key. Every time you revisit a machine learning concept, you’ll understand a little bit more. And before you know it, you’ll know a lot about machine learning. Most importantly, you’ll be able to start putting this newfound knowledge toward changing your career, and changing your life. 

I hope this advice has been helpful and I look forward to seeing you soon in the world of machine learning!