How can we bridge the gap between maths needed for AI and Machine learning with that taught in high schools (up to ages 17/18)?

To put this idea into some more context: The maths behind Machine Learning comprises of four key areas:

It means, we could truly inspire someone with minimal resources to take up Data Science as a profession.

As an educator, the main problem in teaching the maths behind Data Science is:

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We are addressing this problem by working with teachers globally. **

We are creating a maths-based Open source syllabus for learning AI for the next generation.

More details coming soon...

Please contact **ajit.jaokar at feynlabs.ai** for more information.
The name **feynlabs** is inspired by the vision of Richard Feynman as a systems thinker, teacher and a humanist.

Contact**ajit.jaokar at feynlabs.ai **

To put this idea into some more context: The maths behind Machine Learning comprises of four key areas:

- Linear algebra
- Statistics and Probability theory
- Multivariate calculus
- Optimization

It means, we could truly inspire someone with minimal resources to take up Data Science as a profession.

As an educator, the main problem in teaching the maths behind Data Science is:

**Cognitive dependencies**: There are many inter-dependent concepts and to explain something new, you need to explain the dependencies which can be numerous**Cognitive overload**: There are too many things to learn in a short timeframe. Related to this, is the fact that there is too much content out there. While, the content on the Web is excellent in many cases, it can be overwhelming

We are creating a maths-based Open source syllabus for learning AI for the next generation.

More details coming soon...

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