Taking the time to learn the technical ins and outs of machine learning and artificial intelligence is a big step. However, a lot of people do not even make it past the first hurdle. Setting up Python, running in Terminals, learning what a Jupyter Notebook even is. All of these tasks come before the actual process of diving into learning to code in most instances. As a result of these extraneous hurdles, many people do not find it worthwhile to learn. There is no immediate reward or payoff for learning any of these things.
This course is specifically designed for the student that would like to skip that introductory step and dive straight into the code and playing with functional models. Whether you want to learn the basics of machine learning, or you want to run Mixtral itself, this course has a Colab Notebook that will suit your needs.
You will literally not be able to find a majority of these Colab Notebooks anywhere else, as they are ones I have built out myself. Over six pages of notebook links are available to you as soon as you sign up for this course. The course is also broken up into six sections, which cover the six sections of Notebook Resources available to you overall.
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From Apple employee number ~5,000 and working through college as a hardware technician, to a software developer, to a cloud architect, and now AI and ML Sherpa, I have seen it all when it comes to technology development over the past 20+ years. I simply wish to impart that advice in any way that I can.
