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Introduction to Machine Learning - Google Developers

Quick summary of Google Introduction to Machine Learning course
Introduction to Machine Learning - Google Developers
Photo by Markus Winkler / Unsplash

Google developers course here. Here's a quick summary of this very short course:

  • Machine Learning, ML is process of training the software, aka 'model' to make useful predictions or generate content from data.
    • Example given is a weather predictor. Instead of using traditional approach based on very complex equations, feed a ed a model huge amounts of weather data and the model eventually 'learns' the mathematical relationship between inputs and outputs and can then predict the rain [output].
  • ML Categories:
    • Supervised learning - makes predictions after seeing large amounts of data with the correct output, thus learning the relationships between input and output
      • Data is best to be large and diverse
      • Training is the process of the model learning how to predict the output/answer
      • Evaluating is process of comparing actual values with the predicted values [answers] from the model
    • Unsupervised learning - the model works on data without any correct answers and tries to learn rules to categorize the data
    • Reinforcement learning - the model tries to make predictions based on getting rewards or penalties during the learning
    • Generative AI - the model creates content from user input