• @ After catching myself with Python, AKA finish python programmer google cert.

• $ Learn Machine Learning / Deep Learning Fundamentals - in PUBLIC.

  • ML AoI
  • Machine Learning Reddit Page
  • Machine Learning Specialization

• @ 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

• Learn in Public

• % Be Proactive and start performing the job you want before you get the job - in PUBLIC

  • 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

  • build projects and reach out to companies that could use them and give it to them and have them check it out.

  • look at a cool job and try emulate the job in real life compared to the Job Description.

  • $ Idea: Take something you are passionate about, apply machine learning to the topic. Share it in public

  • @ Polish your ML Resume

  • having code, demo, or blog posts on your resume is more impactful than courses

    • What we look for in a resume
    • ML Interviews

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