Learning Machine Learning and Artificial Intelligence with Blast

Hello world!

My name is Victor Onoja Odoh, friends call me Blast. I’m a software engineer with over three years of experience building and maintaining software and writing technical articles.

Like everyone else I was blown away with the advent of chatGPT and subsequently other models like Gemini, Claude, Perplexity, Grok etc.

I’ve always had a passion for tech and engineering in general even before I became a software engineer. One of my goals for 2025 is to gain mastery of machine learning and artificial intelligence. I want to learn everything I can about it and ultimately I want to be able to build my own models and impact the world.

Already as a software engineer, I’ve built some intelligent apps, CoFounderAI, which helps entrepreneurs get from idea to execution as quickly as possible and CocoAI, which helps social media managers schedule and create posts for different social media platforms easily.

To build these apps, I leveraged prompt engineering techniques to Gemini model and both apps work superbly. At the start of 2025 I decided to dive deeper, I wanted to know how LLMs work underneath the hood and I wanted to just know everything I could about Artificial Intelligence.

From reading the Gemini documentation, I found a pretty reliable roadmap for becoming a Machine Learning Engineer or AI researcher. I’m currently on that path and I’m writing these articles, starting from this one to share my knowledge and by sharing my knowledge to gain mastery.

These series of articles would be me sharing everything I learn in my path to becoming a Machine Learning Engineer and AI researcher. These articles would be ideal for software engineers who wish to transition into Machine Learning and AI like me. It would also be ideal for python programmers looking to get into Machine Learning Engineering and AI research.

I’m not an expert yet but I love learning and I’ve also found joy in sharing my knowledge via articles so that’s what I’m doing. My goal here is to deepen my own knowledge by sharing it and find some comrades along the way who will learn via my articles.

If you fit into the category then welcome and I hope by the time we end this series, we’ll be masters of AI, able to build any model we wish and able to contribute in any Machine Learning or Artificial Intelligence research lab.

First off, I’ll start by sharing the prerequisites for becoming a Machine Learning Engineer or Artificial Intelligence researcher. These are not from me, I got them from the Gemini documentation.

1. Foundational Knowledge

• Mathematics: Understand fundamental concepts like linear algebra, calculus, probability, and statistics. If you studied an engineering course in the university like me then you should have this knowledge already otherwise Khan Academy, Coursera, and edX offer excellent free resources.

• Programming: Python is the go-to language for AI. Learn the basics of coding, data structures and algorithms. As a practicing software engineer, I’m quite comfortable with python and I completed an introductory computer science course from Harvard University so I have this knowledge already but Codecademy, freeCodeCamp, and Google’s Python Class are great starting points if you don’t have the knowledge already.

• Data Science Fundamentals: Get familiar with data manipulation, cleaning, visualization, and analysis using libraries like Pandas, NumPy, and Matplotlib in Python. I completed a data science fundamentals course from DataCamp where I got this knowledge you can do the same or take crash courses on YouTube to get this knowledge.

That’s it. To follow these series of articles and to become a Machine Learning Engineer or Artificial Intelligence researcher you need these foundational knowledge because most of what we’ll be doing we’ll be building on this foundational knowledge.

If you don’t have any of the foundational knowledge yet, don’t fret, take a couple of months to get them and you can come back to these articles. What’s worth doing is worth doing well and if the foundation is good, there’s no limit to what you can achieve.

Okay, for those of us that have the foundational knowledge set, we’ll be deep diving into all things machine learning and artificial intelligence.

My reference book currently is Deep Learning with Python by Francois Chollet. It’s a great book written by the inventor of Keras, a machine learning library so we’re learning as close to the horse’s mouth as possible.

I’m halfway through the book and I’ve learnt so much with much more to learn, I’ve built several projects already and by following these articles you’ll be closer to being a Machine Learning Engineer or Artificial Intelligence researcher than ever before.

I’ll stop this article here and in the next article which I’ll be publishing quickly after this one we’ll be starting with deep learning and a history of artificial intelligence and how the science has progressed through the years. Yep straight to the real technical hands-on stuff.

If you’d like to get my articles sent directly to your mail instead please follow this link.

Thanks for reading, see you soon. Bye.