What is Machine Learning ?

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed

We are using machine learning multiple times a day even without us realizing it

  • When we type a query in google search, it tries to provide a auto-complete suggestion that is based on the leanings from the previous search queries
  • Facebook tags our friends in the pictures based on machine-learning
  • Netflix recommends new videos based on our previous viewing history
  • When you call your bank, the virtual assistant (IVR) is powered by machine learning
  • Your Alexa / google home uses natural language detection to convert your queries into text

Machine Learning Terminology

Features

A feature is an input variable—the x variable in simple linear regression. A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features, specified as: x1, x2...xn

In the spam detector example, the features could include the following:

  • words in the email text
  • sender's address
  • time of day the email was sent
  • email contains the phrase "one weird trick

Label

A label is the thing we're predicting—the y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything

Here's an example:

housingMedianAge
(feature)
totalRooms
(feature)
totalBedrooms
(feature)
medianHouseValue
(label)
15 5612 1283 66900
19 7650 1901 80100
17 720 174 85700
14 1501 337 73400
20 1454 326 65500

Model

A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with "spam". Let's highlight two phases of a model's life:

  • Training means creating or learning the model. That is, you show the model labeled examples and enable the model to gradually learn the relationships between features and label.

  • Inference means applying the trained model to unlabeled examples. That is, you use the trained model to make useful predictions (y'). For example, during inference, you can predict medianHouseValue for new unlabeled examples.

Types of Machine Learning Algorithm ?

  1. Supervised Learning
    1. Regression
    2. Classification
  2. UnSupervised Learning
  3. Reinforcement Learning


Next Section: Supervised Learning



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