First week: AI technology, what is AI and what is machine learning? What’s supervised learning, that is learning inputs, outputs, or A to B mappings. As well as what is data science, and how data feeds into all of these technologies? What AI can and cannot do?
Second week: What it feels like to build an AI project? What is the workflow of machine learning projects, of collecting data, building a system and deploying it, as well as the workflow of data science projects? How to carry out technical diligence to make sure a project is feasible, together with business diligence to make sure that the project is valuable before you commit to taking on a specific AI project?
Third week: How such AI projects could fit in the context of our company? Examples of complex AI products, such as a smart speaker, a self-driving car. What are the roles and responsibilities of large AI teams? The AI transmission playbook, what are the five-steps for helping a company become a great AI company?
Last week: AI and Society. What are the limitations of AI beyond just technical ones? How AI is affecting developing economies and jobs worldwide?”
“Artificial Intelligence/Machine Learning field is getting a lot of attention right now, and knowing where to start can be a little difficult.
I’ve been dabbling in this field, so I thought of curating the best resources in one place. All of these are curated based on if it’s an inspiring read or a valuable resource. I hope this curated list help you get started on what you need to know about AI/Machine Learning on a technical level.”