How I went from Apple Genius to startup failure to Uber Driver to Machine Learning engineer. An older article about journey to become ML expert by Daniel Bourke.
I was working at the Apple Store and I wanted a change. To start building the tech I was servicing. I began looking into Machine Learning (ML) and Artificial Intelligence (AI). I began looking into Machine Learning (ML) and Artificial Intelligence (AI).
The article main content is split into:
- How did I get started?
- My self-created AI masters degree
- Getting a job
- Sharing your work
- How much math?
- What does a machine learning engineer actually do?
Here are a few questions a machine learning engineer has to ask themselves daily:
- Context — How can ML be used to help learn more about your problem?
- Data — Do you need more data? What form does it need to be in? What do you do when data is missing?
- Modeling — Which model should you use? Does it work too well on the data (overfitting)? Or why doesn’t it work very well (underfitting)?
- Production — How can you take your model to production? Should it be an online model or should it be updated at time intervals?
- Ongoing — What happens if your model breaks? How do you improve it with more data? Is there a better way of doing things?
Learning online, I knew it was unconventional. All the roles I’d gone to apply for had Masters Degree requirements or at least some kind of technical degree. I didn’t have either of these. But I did have the skills I’d gathered from a plethora of online courses. Along the way, I was sharing my work online. My GitHub contained all the projects I’d done, my LinkedIn was stacked out and I’d practised communicating what I learned through YouTube and articles on Medium.
How do you start? Where do you go to learn these skills? Nice read!
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