AI News, Self-Study Guide to Machine Learning

Self-Study Guide to Machine Learning

In this post I want to put some structure around these activities and suggest a loose ordering of what to tackle when in your journey from programmer to machine learning master.

want to separate beginner from novice here because I want to show that an absolute beginner (a programmer with an interest in the field) has a path before them if they choose.

We are going to tour through each of these four levels and look at resources and activities that can help someone at one level learn more and progress their understanding and skill levels.

They need to be put into action to have the material grounded and integrated into existing knowledge structures like the programming languages they know or the problems they are used to solving.

They are also building the skills of spending a lot of time with data up front, cleaning, summarizing and thinking about the types of questions that it can answer.

Some activities and resources for the intermediate are: An advanced practitioner has written a lot of code either integrating machine learning algorithms or implementing algorithms themselves.

They keep abreast of new developments in the fields and eagerly seek out and learn the nuances of a method and tips passed around from other frontline practitioners like themselves.

This breakdown could be read as a linear path of the technicians journey from beginner to advanced level, it’s intentionally programmer centric.

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