A strategic portfolio can be worth more than an $80K 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.
Most online courses don't work
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 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.
This isn't about concepts or theory. You are probably aware there are plenty of resources to learn the practical and theoretical aspects of machine learning. Instead, this guide helps you navigate and select those resources that are useful for landing an ML job.
About the Author
Emil Wallner is a resident at Google where he works at the intersection of Machine Learning and Arts & Culture. Emil is self-taught and holds a high-school diploma.
Emil has written ML articles and made ML products that have been read and used by millions, and has been featured in Wired and Washington Post as well as 200+ other news outlets and TV channels.
**Any opinions expressed are solely my own and do not express the views or opinions of my clients or employers.
If you've bought a copy **please** rate this guide here on Gumroad and leave a review on Twitter. If possible, tag @emilwallner or use the hashtag #NoMLdegree. That would mean the world to me :)
If you are not 100% satisfied, reply to the download email within 30 days and you'll get a full refund. No questions asked.
Other purchasing options:Amazon: $9.99
Reviews on Amazon:
It is definitely worth more than $9. I am feeling a bit calm and know what I need to focus on. - Vhiz
This is one of those amazing books worth re-reading before an ML project, there are so many types of projects you could do! - Bharat Raghunathan
With so much information available in the field of machine learning, it can be tough for self-directed learners to find their way. @EmilWallner's e-book offers a step-by-step guide for self-learners who want to pursue a career in machine learning. - Richmond Alake
This is a gold mine of advice. - Sanyam Bhutani
"No ML Degree" is really amazing! - Vinayak Nayak
This was a stellar read! You can tell Emil has walked the path himself, he has some very valuable insights. - Radek Osmulski
This book gives honest and real advice (and roadmap) to self-learners, like me, for breaking into ML. Highly recommend! Wish I knew all these early in my career. - Edwin Hung
A 60-page guide on how to land a job in machine learning without a degree.
FormatsEPUB (e.g. Kindle, Google Play Books, and Apple Books) and PDF.
- A 60-page guide on how to land a job in machine learning without a degree.
- Formats EPUB (e.g. Kindle, Google Play Books, and Apple Books) and PDF.