WEBVTT 00:00.830 --> 00:06.770 The mantra of this course is that the best way to learn is by doing, and we will be doing stuff together 00:06.770 --> 00:07.340 throughout. 00:07.370 --> 00:12.350 There will be eight commercial projects which will be applying what you're learning to real business 00:12.350 --> 00:15.380 problems, and they'll be exercises for you to do the same thing. 00:15.380 --> 00:21.050 And I'm hoping that you'll be able to set up GitHub and be able to make your own versions and push them 00:21.050 --> 00:26.030 and send me a PR, a pull request, and I'll be able to merge in your code so that other people can 00:26.030 --> 00:27.320 see your solutions too. 00:27.350 --> 00:32.570 But you'll be able to add this to your GitHub and be able to show yourself that you are building projects 00:32.570 --> 00:34.790 to solve real world commercial problems. 00:34.790 --> 00:38.570 One of our projects I mentioned is going to be a chatbot assistant. 00:38.570 --> 00:40.100 We're going to build one for an airline. 00:40.100 --> 00:43.400 It's going to be able to do things like use tools to look up the price of tickets. 00:43.400 --> 00:48.230 It's going to make audio and it's going to show pictures like this beautiful pop art version of London 00:48.230 --> 00:52.490 showing here on the right as you interact with the chatbot assistant. 00:52.910 --> 00:58.910 We're also going to be building rag pipelines, putting vectors of information in a vector data store 00:58.940 --> 01:03.260 that you'll have running, and we'll be able to look at things like visualizations of the vectors, 01:03.260 --> 01:09.920 mess around with them in 3D space, look into them to understand how different kinds of information 01:09.920 --> 01:15.980 get placed in different locations in vector space, and why that is fundamental to Rag retrieval, augmented 01:15.980 --> 01:16.910 generation. 01:16.910 --> 01:21.800 And then, as I say, the the moment, the big moment in the project will be in week eight, when we 01:21.800 --> 01:27.620 will build an Agentic AI solution that will bring together everything that you've learned about through 01:27.620 --> 01:31.340 the eight weeks for something which will solve a business problem. 01:31.640 --> 01:36.260 And I'll be able to give you some examples of how you can then apply that to solving other business 01:36.260 --> 01:39.260 problems that you might face in the future, or that you might have today. 01:39.320 --> 01:44.660 And you'll be able to watch as agents collaborate, you'll see the memory of the agents and see what's 01:44.660 --> 01:47.180 going on in our agent world. 01:47.210 --> 01:48.890 A lot to look forward to.