Tech

Day 1 of 100 Days of AI

Michael Tefula

Michael Tefula

— 2 min read

Today I’ve started a “100 days of AI” challenge. I’ve used LLMs across a few personal projects here, here, and here. But I’d like to understand the basics of AI and machine learning a bit better.

My motive isn’t to retrain as a ML engineer or data scientist. Instead, I want to challenge myself to go beyond a superficial understanding of one of the greatest technology advancements of our time, particularly given my work in technology investing.

Every day, I’ll aim to do anything from 10 to 90 mins of learning, experiments, and review. Here we go!

Day 1: Overview & Learnings

I’m working my way through an introductory machine learning course by IBM, which provides an AI Engineering Professional Certificate at the end.

I’m about 25% of the way through it, and already, we got to build a simple linear regression model that predicts CO2 emissions based on engine size. I’ve put the GitHub code for this here. Most of the action comes from the code snippet below.

Key takeaways today:

  • Artificial intelligence (AI) is a broad definition that covers computer system that performs intelligent human-like functions.
    • Machine learning is a subset of AI that uses statistical techniques to learn from data and infers patterns and makes predictions.
    • Deep learning is a subset of machine learning and it uses neural networks (inspired by the brain structure) to learn from data.
    • There’s a good explainer of all this here.
  • I’m going to be looking at mostly machine learning for now. This can be split across supervised learning (where you provide data that’s ‘labelled’) and unsupervised learning (where the data is ‘unlabelled’).
  • Popular techniques for ML models include regression and classification (in supervised learning), and clustering and association (in supervised learning).
  • There are a number of open source libraries that make it easier to prepare and build machine learning models. The key ones I’ll probably use a lot are NumPy, SciPy, Pandas, and Scikit learn.

Many concepts feel foreign to me at the moment, but as I spin up a couple of projects and work through tutorials, I should start to get the basics down!

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