Machine learning

Machine learning or machine learning model is a "thing" labeler

• A label could be a yes or no
• A lable could be a price (number)
• A label could be a group
• A label could be a category

To put in short, machine learning is how you put the label on the thing. Then how do we make decisions over them.

Software engineering vs Machine learning

• Software engineering: Knowledge + Data -> Recipe: Biz rule -> Answer
• Machine learning: Data -> Recipe: ML Model -> Answer

ML project

• https://blog.google/outreach-initiatives/sustainability/google-ai-reduce-greenhouse-emissions-project-greenlight/

ML model

• Regression: Whenever we have a number as a label, we will talk about regression model. Eg: Predicting the number of people who will buy a product
• Classification: Whenever we have a category as a label, we will talk about classification model. Eg: IsSpamEmail, IsNotSpamEmail
• Clustering: Whenever we have a group as a label, we will talk about clustering model. Eg: Grouping people based on their behavior. (The group is not existing before)

Generative AI and Machine Learning

Both will have different use cases:

• Machine learning is traditional machine learning model
• Generative AI is a new model Eg: Generative AI can not be used for error-sensitive applications like self-driving cars.