Data Sciencing: The Journey Begins

In recent months, I’ve taken a massive interest in doing some machine learning which is turning me on to the whole world of data science. Not for the typical, “ML, so fashionable” reasons. I have an actual use case I want to solve at work.

Great, so I wanna learn some cool ML tricks to help define and group experts using the secrets hidden in our data. Let’s do a quick google search on how to learn about this stuff…*face melts*

Long story short, everyone and their grandma has got an online course. There’s no shortage of YouTube videos/coursera/EDx/udemy/LinkedIn Learning/kaggle/etc. It’s dizzying. I decided to spite my inner FOMO engineer and just took a rough cut of courses and selected Duke University’s Introduction To Machine Learning using some weak ass selection criteria. Basically, anything that looked like I could reasonably complete it.

The good news, it was a great choice. It had a fairly low time commitment which is essential when you have 3 kids (one of which is a 3 week old infant) and a full time job. I was able to get the work done each week. What made this a great match for me was the repetition, visualization and breakdown of the different ML tools like logistic regression, RNN and CNN to name a few. Pretty easy to digest and internalize for a newb. It alleviated much of the mystique around ML as well as not destroying me with equations and math formulae. The labs were mostly good and maybe there could have been a tiny bit more hand holding on the RNN lab. None the less, it did the trick and I feel smarter.

I’m jazzed about learning all of this. Each time I’m introduced to a new concept, I run off and read about it on wikipedia or find some YouTube videos that speak slowly and uses simple words for my newly forming ML brain to comprehend. I’ll actually take the time to decipher the mathematical equations just to gain some more insight into why things are the way they are.

There’s going to be some method to the madness though. I have an actual path for the learning and I’m actively resisting any analysis paralysis of all the learning resources out there. I’ve got the following going on:

  • I like having some structure to my learning so I’ve signed up for the DeepLearning.AI Natural Language Processing Specialization.
  • I’m practicing as much immersion as I can. For example, if I’m driving, I’ll listen to a podcast or YouTube video discussing things like principle component analysis. I’m trying to fit as much of this into my day that I can.
  • I’m writing down any crazy idea for a ML project that I have. What’s going to accelerate this learning is executing on some of these new skills.
  • I’ve picked up some books to read when I want a break from online learning. I’ll follow up with some reviews when I’ve had a chance to dig in.
  • I started this damn blog to chronicle my progress and keep me accountable.

On that note, apologies for the shitty blogging and very shitty looking website. I’ve got some work to do on sprucing the place up. I’m waaay out of practice on writing altogether and it shows. Expect better in the near future (thumbsup)