No ML Degree PDF
Preview No ML Degree
Learn how to:
- Think like an employer
- Select resources to learn how to program
- Pick a practical course to learn competitive ML skills
- Choose between the six types of projects employers value
- Identify ideas that make you stand out
- The habits to make progress on your projects
- Build a framework to self-evaluate your projects
- Find and email companies that value real-world skills
- Prepare for practical interviews
This guide is for self-learners, but it's also crucial for degree holders looking to strengthen their resumes with portfolio projects.
Online courses can provide structured learning resources but have a marginal impact on employment attractiveness.
Many ML students end up with good interviewing skills but a weak resume. They realize too late that employers don't value a combination of common portfolio projects and online certificates. Hiring managers see the same portfolio projects over and over again.
Autodidacts lack accountability.
Self-learners can find exam answers online and copy-paste existing portfolio projects. That's what many do. Employers can't tell the difference between honest and low-effort learners.
The hard part of the ML self-learning path is not how to gain knowledge but how to create industry credibility. Self-learners need to learn industry-level skills to build unique and impressive portfolio projects. And then target companies that assess and value their real-world skills.
That's what this guide is all about.
What this guide is not:
This isn't about concepts or theory. You are probably aware there are plenty of resources to learn machine learning. Instead, this guide helps you navigate and select those resources to help you land an ML job.
This isn't about titles. Many want to study an extensive curriculum, do exams, and earn a certain label. Instead of working toward a broad ideal, this guide is about learning specific skills that employers want to pay you for.
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