It’s 11:30 p.m., and instead of sleeping (I feel sleepy :)), I am rather excited to jump back on the Machine Learning (ML) journey I’ve been meaning to take at this stage of my PhD. A deep dive to really understand the basics of ML. Not even the deepest waters, but you know, deep enough to apply and explain introductory concepts to other people. And… drumrolls, I found a good companion book and EdX courses (1 and 2) to fall back on for the journey.

This moment matters for two reasons:

• First, as I keep building my data science skills, I find Machine Learning increasingly exciting and you might say, well-fitting to my passion for econometric analysis. Therefore, one of my goals is to finish my PhD not just familiar with ML, but confident enough to take on projects and maybe even teach it someday.
• Second, about two months ago, ChatGPT and I put together a weekly study plan to help me quickly deepen my understanding of ML basics. But life happened and I only made it through two units of Microsoft Learn’s ML course before getting sucked into the depths of ongoing research.

Now, with my stack of resources and a fresh dose of motivation, I’m hopeful this journey will have fewer interruptions. Perhaps, with the book, I can squeeze in a few pages before bed, even on busy days.

For some structure (we love structure), I’m going to apply a strategy my supervisor used in a Probabilistic Programming course last year — setting a goal to read a chapter in one or two weeks, depending on the volume, and work on the labs of the read chapter in the following week. The book, Introduction to Statistical Learning looks pretty dense, so we use the structure but stay flexible to adjust the plan if need be.

To begin, my target from today to 27th July is to read the Introduction and Chapter One.

Do not hesitate to reach out to me if you’d like to join this journey.