[Inflearn Award Bestseller] How to Become an AI Automation Expert Without Coding, The Complete n8n Guide
Do you still code directly these days? Now is the era where even vibe coding is too much hassle! Become an AI automation expert with n8n without coding. A practical automation workflow design guide you can apply immediately at work Action-oriented automation strategies that maximize work efficiency. Build intuitive workflows with n8n and experience workplace innovation
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Level Beginner
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Edited
A new section has been added to the course.
Hello, this is Dr. Nam.
We've added a new section to our n8n Complete Guide course: How to Become an AI Automation Expert Without Coding .
The original plan was to provide this lecture as just one additional lecture, but as the lecture volume increased and we tried to make it more useful, there were now three lectures, and rather than providing the three as separate additional lectures, we thought it would be better to provide them as one section, so that's what we ended up doing.
The section name was set to Implementing an Agent Using Public API, and 3 additional lectures totaling 1 hour and 42 minutes were added, and the following structure was created.
In the first lecture, we will learn about how to use the apartment sales information API and apartment monthly rent transaction information API among the public data APIs provided by https://www.data.go.kr in N8N, and the overall process of applying them to obtain gap investment data.
In the second lecture, we will cover how to collect the population of the neighborhood where the apartments obtained in the first lecture are located and the GPS data of the apartment addresses using the Statistics Korea API and the Ministry of Land, Infrastructure and Transport V WORLD API, and add the data to the final apartment investment analysis report.
In the third lecture, we will implement an automated function that visualizes past transaction history as an AI report based on the apartment gap investment information used in the first lecture. After repeatedly calling the monthly actual transaction API to extract the necessary data, we will complete the price change analysis graph of past sale/monthly rent data and output an HTML report based on that information .
Apartment Gap Investment Information Report Sample Link
The lecture includes some coding to organize the collected data, but since Python itself is an intuitive language, I think it will not be too difficult to study with AI such as ChatGPT. I think that if you understand this lecture, you will be able to implement expansion with more diverse methods and functions. I hope that many of you who watch the lecture will be able to handle AI Agents skillfully, and that Korea, although its AI infrastructure is still lacking, will become an AI powerhouse in terms of ideas and utilization.