This is the repository for the LinkedIn Learning course Learning No-Code AI. The full course is available from LinkedIn Learning.
One of the most appealing things about no-code is its wide range of use cases and applications. No-code can help you build a website, automate a process, manage a project, or write an interactive guide. It can also help you create AI models for machine learning, voice recognition, natural language processing, and more. In this course, instructor Sudha Jamthe gives you an overview of how and when to use no-code AI to better meet the needs of your business.
Explore the basics of no-code AI platforms so you can choose the platform that’s right for you. Learn how to build a no-code AI computer vision model in three easy-to-follow steps, developing your understanding of the no-code AI ecosystem, its tools and rules, and the various roles you can play if you’re looking for a fresh opportunity. By the end of this course, you’ll be ready to use no-code AI to build new models that work for you.
The tire defect dataset used in the Chapter 2 Challenge/Solution is avaiable to download here via Kaggle. Note that, for optimization purposes, we used only a subset of the datase to train the model.
The handout for choosing a No-Code AI platform discussed in Chapter 3 is available in this repo here.
The Telco-Churn-Dataset for Chapter 3, Video 4 Demo: No-Code AI with Machine Learning is located on Kaggle here, and originally available from the IBM repo here.
Sudha Jamthe
Check out my other courses on LinkedIn Learning.