#DataTalksWithSadeeq
Thread!
1. Understanding the concept and usefulness of learning AI/ML;
2. Learning about the tools and best practices;
3. Getting your hands dirty with constant practice and real-world problems.
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(Keep reading for the detailed answer, tips & links!)
1. Understand ML from its First Principle - Intro & concepts
- classroom.udacity.com/courses/ud359
- coursera.org/learn/machine-…
2. Learn some basic or more Python
- Take as much courses as you can here: datacamp.com/tracks/data-sc…
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udemy.com/course/python-…
4. Join Local AI Meetups/Communities such as @DataScienceNIG or @AISaturdayLagos , and start submitting entries for @kaggle and @ZindiAfrica competitions
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Otherwise, any or all of @Udacity Nanodegrees will surely be a huge boost (they still have first month free):
- udacity.com/course/ai-prog…
- udacity.com/course/data-sc…
- udacity.com/course/machine…
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7. ML Models are hardly deployed On-prem theses days, so learn about Cloud Platforms and Serving ML models via APIs. Popular ones are @Azure @googlecloud & @awscloud. Get certified if you can!
Don't JUMP to Deep Learning, if you've not fully understood basic ML or don't have a good grasp of Neural Networks.
9. Read AI/ML textbooks - Finish one, or at least make sure you are ready to completely abandon it b4 picking another.
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When you feel comfortable, produce, review & publish AI/ML Research. Also, endeavor to contribute to open-source AI/ML projects.
Start teaching whatever you know in ur small circle, then big events.
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Own it, share it!
END. Kindly RT!