Alexander Garcia
Expanding my knowledge of Artificial Intelligence (AI) through DataCamp
Read time is about 16 minutes
Alexander Garcia is an effective JavaScript Engineer who crafts stunning web experiences.
Alexander Garcia is a meticulous Web Architect who creates scalable, maintainable web solutions.
Alexander Garcia is a passionate Software Consultant who develops extendable, fault-tolerant code.
Alexander Garcia is a detail-oriented Web Developer who builds user-friendly websites.
Alexander Garcia is a passionate Lead Software Engineer who builds user-friendly experiences.
Alexander Garcia is a trailblazing UI Engineer who develops pixel-perfect code and design.
I've been a Frontend Engineer for the past 10 years. I have experience in a smattering of other areas like backend, security, and even some web3. While I love Frontend Engineering as its always been my passion and expertise for the past decade I want to expand my knowledge with new skills.
Most of the time I have been so busy trying to reach deadlines at work that I've found myself neglecting my self development through books and coursework.
Mostly I'm writing this as an accountability reminder for myself. So going forward I plan to expand my skills to Artificial Intelligence and Machine Learning (AI/ML) as that's where I foresee more value coming from. From past experiences I've been leaving money on the table by not expanding my technical skills.
Let me share the specific moment that made me realize I was leaving money on the table. While working at VA.gov, I came up with an idea to create a Fraud Risk Engine—a system that could rate each authenticated user based on their trustworthiness to prevent potential fraud. It seemed like a perfect solution to a real problem we were facing.
The concept was solid: analyze user behavior patterns, authentication history, transaction anomalies, and various other signals to generate a real-time risk score. But here's the thing—I knew what needed to be built, I could design the interface and user experience, I understood the product requirements... but I lacked the technical skills to actually build the machine learning models that would power it.
I had to watch that idea sit on the shelf because I didn't have the AI/ML knowledge to execute it. That stung. And it wasn't just about professional frustration—it was about recognizing a pattern in my career where I could see opportunities but couldn't seize them because of skill gaps.
That's when it clicked: having a decade of frontend expertise is valuable, but in today's landscape, being able to integrate AI/ML capabilities into applications is increasingly becoming table stakes. I was limiting myself.
My plan is to work through and complete the following certifications for 2026 and apply that new knowledge in both personal and professional ways.
I'm going to document my journey with each course. But since I already finished my Associate Python Developer course I'm going to start with the Associate AI Engineer for Developers course.
While I'm no longer on the team working on the fraud detection engine concept, my new focus will be applying AI/ML to quantitative finance. Specifically, I'm fascinated by the intersection of algorithmic trading and market prediction.
The dream? Building a trading bot that makes money for me while I sleep. I know that sounds like every programmer's fantasy, but hear me out—this is the perfect application for combining my frontend engineering background with new AI/ML skills.
I'm interested in all avenues of quantitative finance, but algorithmic trading is what really gets me excited. The idea of creating systems that can identify patterns in market data, make informed decisions based on statistical models, and execute trades automatically is incredibly compelling. It's part engineering challenge, part puzzle-solving, and part financial strategy.
Beyond the personal quant finance project, I'm also exploring whether I'd genuinely enjoy doing AI/ML work professionally. I want to expand my knowledge and skill-set to see if this is something I'd want to pursue in a full-time role. Maybe I'll discover a passion for it, maybe I'll decide frontend + AI integration is my sweet spot, or maybe I'll just become a better engineer who understands how to evaluate and implement AI solutions. All of those outcomes are valuable.
Let's be realistic about this: I'm committing to 5-10 hours per week. I've seen too many people (including past versions of myself) set unrealistic goals like "I'll study 3 hours every day!" only to burn out within two weeks.
At my current company, we have to create SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound), and I'm treating each course as one SMART goal with quarterly deadlines. This gives me structure without overwhelming pressure. Some quarters might see faster progress depending on work intensity, but having that framework keeps me accountable.
My approach is to balance theory with practice. As someone who learns best by building, I plan to create projects that tie to the theory I'm learning. This isn't just about collecting certificates—it's about developing genuine competency.
That said, I'm being intentional about not letting this impact my team at work. The whole point of taking advantage of slower periods is that I have the bandwidth. If things pick up, I'll adjust. The goal is growth, not burnout.
I'm not naive about this. There are real obstacles ahead:
Let's be honest—anyone that knows me knows that a.) I'm not good at math and b.) I hate math. The good news is that there are tons of resources for this, and DataCamp does a decent job of explaining the math as you go. But I'm not going to sugarcoat it—this will be the most challenging part for me.
I'm always busy with something - either the house, family obligations, or work - something is always eating away my time. I have to really carve out time to achieve these goals
There's always a gap between understanding theory and implementing it in practice. I can watch all the videos about neural networks I want, but until I actually build one, debug it, optimize it, and see it work (or fail), I haven't really learned it.
This is where my frontend engineering background will help. I'm used to the cycle of learning a concept, trying to build with it, failing, debugging, and eventually shipping. I know how to persist through that frustration.
While I'm entering the AI/ML space as a beginner, I'm not starting from zero. My decade of frontend engineering gives me some distinct advantages. I know how to write clean maintainable code, I've coded in Python before. I also know product thinking, UX/UI work, and I've successfully deployed web applications before. Although, deploy ML models might be a bit different, I think I have a unique perspective on this.
The combination of frontend expertise + AI/ML knowledge is powerful. I'm not trying to become a research scientist publishing papers—I'm trying to become an engineer who can build intelligent applications that people love to use.
Completing the certifications is necessary but not sufficient. Here's what real success looks like to me:
But here's my north star: by the end of 2026, I want to have launched and monetized an AI-powered product. Probably something in the quantitative finance space, possibly that trading bot I mentioned. It doesn't need to make millions—but it needs to be real, launched, and generating some level of income.
That would prove I've successfully bridged the gap from frontend engineer to someone who can build, deploy, and monetize AI applications. That's the outcome that would make all of this worth it.
This blog post is step one of my accountability system, but it's not the only mechanism:
Quarterly Deadlines: Each course completion is tied to a specific quarter in my SMART goals at work. Missing a deadline means explaining why in my performance review. That's motivating.
Project Commitments: For each course, I'm committing to build something that demonstrates what I learned. The specifics will vary by course content, but the expectation is clear: no completion without application.
Public Documentation: I plan to write follow-up blog posts as I complete each major milestone. There's something about knowing people might read about your progress (or lack thereof) that creates healthy pressure.
2026 is going to be a transformative year. By December 2026, I'll have completed multiple certifications, built several projects, and hopefully launched that monetized AI-powered product I've been dreaming about.
But more importantly, I'll have transformed how I think about problems and solutions. I'll see opportunities where I currently see limitations. I'll be able to prototype ideas that are currently beyond my technical reach.
Will it be easy? Absolutely not. Will I struggle with math concepts, fight through frustration, and occasionally question why I'm doing this? Probably. But that's part of growth.
I'm choosing to invest in myself during this period of relative calm at work. Not everyone has that opportunity, and I don't want to waste it. Years from now, I want to look back at 2026 as the year I significantly leveled up my capabilities.
I'll be documenting this journey as I go. Next up: diving into the Associate AI Engineer for Developers course and sharing what I learn along the way. Stay tuned.
Want to follow along? I'll be posting updates as I complete each certification and build projects. Feel free to reach out if you're on a similar journey—we can learn together.