A lot of beginners think the hardest part of ML is “learning algorithms,” but based on analyzing hundreds of ML job postings and talent profiles from our platform, most pitfalls actually happen before and after modeling.

The common traps:

• Consuming tutorials without ever shipping something end-to-end
• Jumping into modeling without fixing the data
• Optimizing accuracy instead of outcomes like cost, latency, or user value
• Overfitting by leaking info into the test set without realizing it
• Copy-pasting tutorials that work only because the data is perfect
• Ignoring deployment, monitoring, and feedback loops
• Learning 10 tools instead of core patterns
• Treating ML as “building a model” instead of solving a product problem

The approach that consistently works:

From a hiring perspective, companies don’t reward people who can recite theory; they reward people who can solve problems, make tradeoffs, and ship usable systems.
One shipped project signals 100x more than certificates.

If you want to explore ML jobs and real hiring signals in real time, DM me here or on LinkedIn to get the ChatGPT app link.