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@ After catching myself with Python, AKA finish python programmer google cert.
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$ Learn Machine Learning / Deep Learning Fundamentals - in PUBLIC.
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@ You don’t need another course (after learning skills). Start applying ML concepts through building real-impact projects in PUBLIC: best way to learn
- need to “go off the happy path” so you can learn the highly valuable specific knowledge that comes from hours of troubleshooting, data mining, and stack exchange
- and sharing your experiences in public is going to be the way to really learn and increase your communication skills and ML expertise.
- Acquire through personal experimentation and public-sharing
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% Be Proactive and start performing the job you want before you get the job - in PUBLIC
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submit pull requests for the company codebase that you want to work at.
- when submitting pull requests look at their documentation or use Ivy ML Library as a template
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build projects and reach out to companies that could use them and give it to them and have them check it out.
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look at a cool job and try emulate the job in real life compared to the Job Description.
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$ Idea: Take something you are passionate about, apply machine learning to the topic. Share it in public
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@ Polish your ML Resume
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having code, demo, or blog posts on your resume is more impactful than courses
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review of all my machine learning resources that is inside Google Chrome. organize into a separate folder in obsidian and re-work my Software Engineer in 2 years folder to be a more accurate representation of my focus and what I want to start doing.
this video is going to be my new over-arching roadmap for learning, and anything that is MOOC related and is not beginner ML; is going to be put on the back-burner. - Daniel Bourke’s GOAT-ed roadmap with resources