Here is a list of recommendations and resources for those who aspire to join the AI Apprenticeship programme or want a career in AI.
- Hone your Python and/or R skills. There are many excellent online resources. There is no need to go for a paid course (Seriously, if you need to attend a Python or R course to learn the language, then likely you are not the candidate we are seeking).
- Watch the excellent and legendary “Elements of Statistical Learning” series by Hastie and Tibshirani. See this post from R-bloggers
- Get familiar with Keras for deep learning (there is more to AI then DL, but this is a good start).
- Get familiar with Spark as a data platform for your AI/ML workloads
- Get an Azure (or AWS, GCP) account and go build data products in the cloud.
- Learn to package and distribute environments in Docker.
- Learn to manage a project with Git on GitHub, Bitbucket etc.!
- Concentrate on Numerical Linear Algebra and Statistical Computing because these power AI libraries today. Greenbaum and Chartier’s textbook Numerical Methods is a nice theoretical introduction. For practical implementation, familiarise with the Tensorflow and Pytorch AND ALSO low-level Numpy, Scipy libraries. If you’re at a more advanced level experiment with Numba and Cython more optimising how fast code runs.
- For a complete picture of what AI really is, nothing beats Artificial Intelligence: A Modern Approach by the gurus Peter Norvig and Stuart Russel.
- No projects from the office or idea what to build? Go to your local community and grassroots organization and offer to work on a project to help them build something! Until you build a real-world project, you will never experience the non-technical issues on the ground.
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