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WEBVTT
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The mantra of this course is that the best way to learn is by doing, and we will be doing stuff together
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throughout.
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There will be eight commercial projects which will be applying what you're learning to real business
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problems, and they'll be exercises for you to do the same thing.
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And I'm hoping that you'll be able to set up GitHub and be able to make your own versions and push them
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and send me a PR, a pull request, and I'll be able to merge in your code so that other people can
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see your solutions too.
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But you'll be able to add this to your GitHub and be able to show yourself that you are building projects
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to solve real world commercial problems.
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One of our projects I mentioned is going to be a chatbot assistant.
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We're going to build one for an airline.
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It's going to be able to do things like use tools to look up the price of tickets.
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It's going to make audio and it's going to show pictures like this beautiful pop art version of London
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showing here on the right as you interact with the chatbot assistant.
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We're also going to be building rag pipelines, putting vectors of information in a vector data store
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that you'll have running, and we'll be able to look at things like visualizations of the vectors,
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mess around with them in 3D space, look into them to understand how different kinds of information
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get placed in different locations in vector space, and why that is fundamental to Rag retrieval, augmented
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generation.
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And then, as I say, the the moment, the big moment in the project will be in week eight, when we
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will build an Agentic AI solution that will bring together everything that you've learned about through
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the eight weeks for something which will solve a business problem.
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And I'll be able to give you some examples of how you can then apply that to solving other business
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problems that you might face in the future, or that you might have today.
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And you'll be able to watch as agents collaborate, you'll see the memory of the agents and see what's
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going on in our agent world.
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A lot to look forward to.