Think like an employer and stand out in the job market by:
- Selecting the best resources to learn programming and machine learning skills
- Identifying the types of projects that employers value most
- Bringing unique ideas to the table
- Developing habits that help you make progress on your projects
- Creating a framework to evaluate and improve your projects
- Finding and contacting companies that value real-world skills
- Preparing 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.
Reviews on Amazon:
Stand Out in the Machine Learning Job Market
Most online courses alone are not enough to make you an attractive job candidate. While they can provide structured learning resources, they often have limited impact on your employment prospects.
Many machine learning students have strong interview skills but a weak resume. They often realize too late that employers don't value a combination of common portfolio projects and online certificates. Hiring managers have seen the same projects over and over again.
Self-learners can struggle with accountability. It's easy to find exam answers online and copy-paste existing portfolio projects, but employers can't always tell the difference between honest, hardworking learners and those who took the easy way out.
To truly stand out in the machine learning job market, you need to go beyond just gaining knowledge. You need to build industry-level skills and create unique, impressive portfolio projects that show off your real-world skills. And then you need to target companies that value those skills.
That's what this guide is all about – helping you build industry credibility and succeed in the job market as a self-learner.
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
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.