DataScience Classroomnotes 04/Feb/2022

Introduction to Machine Learning

  • What is Machine Learning (ML)
  • Machine Learning is study of computer algorithms that improve experience
  • Seen as subset of Artificial Intelligence (AI)
  • ML algorithms build a mathematical model based on sampe data in order to make predictions or decisions without being explicitly programmed
  • Also referred to as Predictive Analytics in its applications business problems.
  • Where is ML Used?
  • Used in wide variety of applications
    • Email Filtering
    • Loan Prediction
    • Sales Forecasting
    • Movie Recommendation
  • Why Machine Learning?
  • Programs to solve problems like facial recognition are very hard
  • Instead of writing a program, collect lots of examples that specify the correct output for a given input
  • An ML algorithm then takes these examples and produces a program that does the job
    • Program works for new cases & ones it trained on
  • Requirements of ML
  • Some pattern must exist between the input and output of data (the relationship cannot be completely random)
    • Lottery winning numbers cannot be predicted using ML
  • Enough data that needs to be present to discover this pattern
  • No easily definable set of rules or mathematical formulas should exist that can explain the relationship between input and output of the data
    • If it did, we can use traditional programming
  • Recognizing patterns
    • Facial identities
    • Handwritten or spoken words
  • Recognizing Anamolies
    • Unusual sequence of credit card transactions
    • Unusual patterns of sensor readings in nuclear power plant
    • Unusual sound in the car engine
  • Prediction:
    • Future Stock prices or currency exchange rates
    • Employee attrition
    • Loan defaulting
  • More Examples:
    • Spam filtering, Fraud detection
    • Recommendation Systems
    • Autonomous cars
    • Speech recognition
    • Image classification/tagging
    • Language translation
    • Price Prediction
    • Advertising: Ad click prediction
    • Playing games (Chess, Go, StarCraft etc.)
  • Machine Learning Approaches:
  • Traditionally divided into three categories
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning

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