Machine Learning is a latest buzzword floating around. It deserves to, as it is one of the most interesting subfield of Computer Science. Machine Learning is used anywhere from automating mundane tasks to offering intelligent insights, industries in every sector try to benefit from it.

Some Examples for Machine Learnings are :

  • Prediction — To compute the probability of a fault, the system will need to classify the available data in groups.
  • Image recognition — Used for face detection in an image or video.
  • Speech Recognition — It is the translation of spoken words into the text. It is used in voice searches and more.
  • Medical diagnoses — ML is trained to recognize cancerous tissues.
  • Financial industry and trading — companies use ML in fraud investigations and credit checks.

Classification of Machine Learning

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning
Machine Learning Use Cases – The Nerd Vibe

Terminologies

Labels

label is something that we’re predicting—let’s call it y variable in simple linear graph. The label could be the future price of a product you’re predicting, the kind of animal shown in a picture, or just about anything.

Features

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

Examples

An example is a particular instance of data, x (where x is a vector). There are two categories:

  • labeled examples
  • unlabeled examples

labeled example includes both feature(s) and the label. That is: labeled examples: {features, label}: (x, y)

A labeled example is used to train a model, where we teach the machine the difference between the features. Where we say that an email is a “Spam” or “Not Spam”.

An unlabeled example has features but not the labels : unlabeled examples: {features, ?}: (x, ?)

Once we’ve trained our model with labeled examples, we use that model to predict the label on unlabeled examples.

Models

A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with “spam”. There are two phases in a model:

  • Training means creating or learning(where the label learns the relationship between label and feature) the model.
  • Inference means applying the trained model to unlabeled examples. That is, we use the trained model to make predictions (y').

Regression vs. classification

regression model predicts continuous values. For example, regression models make predictions that answer questions like the following:

  • What is the value of a house in California?
  • What is the probability that a user will click on this ad?

classification model predicts discrete values. For example, classification models make predictions that answer questions like the following:

  • Is a given email message spam or not spam?
  • Is this an image of a dog, a cat, or a hamster?

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