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Module 1

  • Knowledge engineering: Knowledge + Software = Decision
  • the model should human-like decision. if not then debug until it do.
  • AI > ML > DL
  • Machine learning is a subset of artificial intelligence or AI. Machine learning uses data, and this data is going to be used to train the model, and the model is then used for predictions.

Module 2

tl;dr

  • key terms
  • framework for ml
  • ml algorithms for business problems
  • ml pipeline

how amazon get into ML

  • Amazon used machine learning to improve the whole routing system.
  • ML pipeline
    • collecting and integrating data
    • prepare the data,
    • visualize it for analysis,
    • select the features you (engineers) want to use
    • train your model,
    • evaluate it
    • and deploy it.

example: creditcard transactions

  • fraud detection

binary classification

  • ex. customer calls service and the ML should predict if customer issue is calling about his new TV or not. so the binary classification TV or not TV.

regression problem

  • we are looking for continous values such as number: 1, 2, 3, ...
  • ex. predict the price of a product

multiclass problem

  • more then two classes

_The machine learning model's job during training is to learn which of these features are actually important to make the right prediction for the future. _

If the value you're looking for is know, like in a supervised learning, then that prediction is called a label. But if the value isn't known, like in unsupervised learning, then it's called a target.

Sources, Links

  • coursera: Getting Started with AWS Machine Learning