Understand what open weights actually let you do, run a model on your own laptop, and build one small tool that would not have been possible through a hosted chat window.<\/p>\n
Students who can read a little code and want to know how these models work underneath, and freshers targeting AI-adjacent roles where "I have run and tuned a model" is a real differentiator.<\/p>\n
4<\/strong> modules and 15<\/strong> lessons, arranged so each one ends with something you have built. Every lesson carries a worked example and a practice task \u2014 the practice is the course, the reading is only the setup. Plan for 5 weeks \u00b7 about 4 hours a week<\/strong>.<\/p>\n The full syllabus \u2014 every lesson, its example and its practice task \u2014 is in the Course curriculum below. Nothing is locked and nothing needs an account.<\/p>\n Titles vary between companies; the evidence does not. A deployed project, a set of queries you can explain, or a case study with real testing behind it is what a fresher interview has to work with.<\/p>\n Not for a small model. A modern laptop with 16GB of memory runs a 3B model acceptably. A GPU makes the larger models usable, and renting one by the hour for a few days is enough for this course.<\/p>\n Not compared with a frontier model, and that is not the point. It is good enough to be private, free and yours, which are different advantages.<\/p>\n Every other course uses somebody else's model over the internet. This one is about owning the model \u2014 which is the only route to privacy, offline work, product integration and fine-tuning.<\/p>\n Three things: an offline summariser for your own documents, a saved set of prompts you wrote and tested on your own work, and a Way2Fresher certificate naming the course. Meta also publishes its own learning material for the tools it makes, and the rail on this course page links to it.<\/p>\nYour weekly routine<\/h2>\n
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What you will have built by the end<\/h2>\n
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Where this leads for a fresher<\/h2>\n
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Frequently asked questions<\/h2>\n
Do I need a graphics card?<\/h3>\n
Is a local model good enough to use daily?<\/h3>\n
How is this different from the other AI courses here?<\/h3>\n
What will I have at the end of this course?<\/h3>\n