If you’re anything like me, starting a machine learning project for the first time can feel like opening a 10,000-piece puzzle — except half the pieces feel like they’re missing.
Whether you’re a beginner or have already trained your fair share of models that didn’t quite work (we’ve all been there), knowing where to start could save you a ton of confusion and caffeine.
Approaching ML with a process is what will help you avoid feeling like you’re losing your sanity.
It will also get you one step closer to getting something that works well enough to brag about on LinkedIn.
Here’s a simple, no-nonsense 5-step way to tackle ML problems—one that’s reusable across any project.
📗 Step 1: Define The Freaking Problem
You know, this part seems like common sense, but you’d be surprised how often beginners rush through it.
Knowing the problem is the FOUNDATION for everything that comes after it.
Ask yourself whether you can narrow the problem down to a single sentence.
Consider:
What exactly am I trying to predict or classify?
Why does it matter?
What would “success” look like here?
Example: “I want to predict if a customer will cancel their subscription.”
The next step after that is to refine this so that' it’s measurable — “I want to predict churn with at least 80% accuracy using customer activity data.”
This type of clarity will tell you what data to collect, how to measure performance, and whether your project is even possible.
📚 Step 2: Prepare Data
Let me let you in on a little secret (unless you’re a data scientist).
Data Scientists spend way less time tweaking fancy models than prepping messy data. Think renaming columns, handling missing values, cleaning duplicates — the stuff that makes your dataset less of a horror movie.
As boring as it sounds? Well, it may not be glamorous…but great models are built on boring groundwork. You’re teaching your algorithm to learn — give it clear examples.
Preparing data should be treated like detective work; the cleaner and clearer your dataset, the smoother everything else goes.
📚 Step 3: Try A Few Algorithms (aka Speed-Dating For Models)
You don’t need to pick the perfect model right away — this phase is about trying a few different ones out quickly to see what clicks.
That means training a few algorithms on the same data to compare performance. Maybe a decision tree wins this round. Or perhaps a random forest steals the show.
The key is not to get too committed to any one model yet — you’re still exploring your options. A real pro doesn’t fall in love too early (with models, at least). Double meaning, perhaps?
Keep it fast, simple, and iterative. And remember, sometimes the boring-looking models outperform the fancy deep learning ones for smaller datasets.
📚 Step 4: Tune & Improve
Once you have found “the one”, now it’s time to obsess a little.
Tuning is where you adjust your model’s parameters to squeeze every last drop of performance out of it.
This is the part where you’re basically asking: “What combination of knobs makes my model stop doing dumb things?”
Things to consider:
Hyperparameter Tuning: Learning Rate, Regularization, Number of layers, etc.
Feature Engineering: Creating better input variables that capture the essence of your data.
Cross-Validation: Testing your model on different subsets of data to ensure it’s not just memorizing.
Small tweaks can yield huge gains. But be careful — models can be as moody as humans. The smallest change can make them behave completely differently!
📚 Step 5: Present & Reflect
Congratulations! Are we finished?
Well, not quite.
Once your model is working well, the next step is to translate its performance into something humans can understand.
Visuals help — confusion matrices, ROC curves, feature importance plots. Summarize what worked, what didn’t, and what you’d try next time.
These types of reflections are incredibly useful — they allow you to understand the why behind your model’s accuracy (or lack of).
📚 Conclusion
Machine learning can feel overwhelming, but a structured approach can keep chaos in check.
Start with your problem → Clean your data → Test Models → Tune → Reflect & Present.
Building ML systems is less about magic and far more about thoughtful consideration and preparation. So ultimately, what do I recommend?
Build more projects!
The more repetitions you run through this cycle, the more natural it becomes…until “building a model” feels like second-nature muscle memory. At that point, it might really feel like magic, after all.
If you want me to write a post on what projects you should create specifically (or heck, maybe I’ll even walk through one with you), then just reply “PROJECTS” to this email.
Until the next epoch.
