Becoming an AI engineer requires a set of technical skills that span across various domains of computer science, mathematics, and software engineering. In this blog I will introduce a learning guide for a person who is interested in diving into the of AI. But before talking about techical stuff you need to remember you need to learn to collaborate with people and contrbute for reserches even if annotating or labeling a dataset. This things seem less valuble and everyone wants to dive into model training part but dataset is the most important part of the work
Find a local Group
If you’re looking to accelerate your learning pace, working with groups can be incredibly beneficial. Below are some groups that might be of interest to you:
The Learning path
Dont forget to apply for financial aid if you cant pay for this courses.
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Mathematics for Machine Learning Specialization
- Beginner level
- Include three small courses
- Takes 1 month if you dedicate 10 hours per week.
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Machine Learning Specialization
- Beginner level
- Include three courses
- Takes 2 month if you dedicate 10 hours per week.
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- Intermediate level
- Include Five courses
- Takes 3 month if you dedicate 10 hours per week.
Additional courses
you can ignore this courses if you know them.
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Python for Data Science, AI & Development
- Beginner level
- 26 hours (approximately)
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Google IT Automation with Python Professional Certificate
- Beginner level
- focus on Git/Github and you can slowly do the rest
Make sure you push all your exercises to github with readmes. participate in kaggle compitations.
Check out the scholarships offered by AMMI if you meet their qualifications. This opportunity can provide you with some of the best machine learning knowledge available.
I will create another blog if you want to specicialize on specific topic after finalizing this list.