What is Machine Learning? Google Professional Data Engineer GCP

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  2. What is Machine Learning? Google Professional Data Engineer GCP
  • an application of artificial intelligence
  • where a computer/machine learns from the past experiences (input data)
  • and makes future predictions.
  • The system performance should be at least human level.
  • ML provides enables machines to learn autonomously based on experiences, observations and analysing patterns within a given data set without explicitly programming.
  • Process – we input a data set, machine will learn by identifying and analysing patterns and learn to take decisions autonomously
  • Example – Facebook’s facial recognition algorithm


All ML algorithm have three components:

  • Representation: how to represent knowledge like decision trees, sets of rules, etc.
  • Evaluation: how to evaluate candidate programs (hypotheses) like accuracy, prediction and recall, likelihood, etc
  • Optimization: how candidate programs are generated or the search process like combinatorial optimization, convex optimization, constrained optimization.

Types of Learning

There are four types of machine learning:

  • Supervised learning: (or inductive learning) Training data includes desired outputs like identify spam, learning is supervised. It is most mature and  Defined as – if data is (x) and the output is (f(x)), goal is to learn the function for new data (x). Techniques include
    • Classification: when the function being learned is discrete.
    • Regression: when the function being learned is continuous.
    • Probability Estimation: when the output of the function is a probability.
  • Unsupervised learning: Training data does not include desired outputs like clustering.
  • Semi-supervised learning: Training data includes a few desired outputs.
  • Reinforcement learning: Rewards from a sequence of actions.