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INTRODUCTION TO MACHINE LEARNING

 MACHINE LEARNING

Machine learning is nothing but training the machine with data in order to predict the correct output and to give us better accuracy.

TYPES OF MACHINE LEARNING

  1. SUPERVISED MACHINE LEARNING
  2. UNSUPERVISED MACHINE LEARNING
  3. REINFORCEMENT LEARNING 

SUPERVISED MACHINE LEARNING

  1. In supervised machine learning, we have both X and Y labels.
  2. Here, X is an independent variable and Y is a dependent variable.
  3. Based on the features of X we are going to predict the output Y.

  • Here X features are( Outlook, Temp, Humidity, Windy) based on these factors we predict the output or we also call it as target variable Y which is (Play).
  •  X is independent.
  •  Y is dependent on X that means Y which is (Play) can only be predicted based on the X features which are ( Outlook,Temp, Humidity, Windy).
Considering X features we can say whether we can Play on those conditions or not.


TYPES OF SUPERVISED MACHINE LEARNING

Supervised machine learning is further divided into:

  1. CLASSIFICATION
  2. REGRESSION

CLASSIFICATION

  • In classification, the output will be the probability of occurrence.
  • Classification deals with discrete data.
  • In classification, the output will be in the form of (0 or 1),(spam or Not_spam),(cancer or Not_cancer)

  • Here admitted is the output variable which has the values 1 and 0 means based on the X parameters (GMAT, GPA,work_exp) we are predicting the output variable Y which is (admitted)
  • 1 means admitted
  • 0 means not admitted
  • In simple terms, the output will be in the form of categorical data
  • Some of the examples are
    • cancer prediction( cancer or not-cancer)
    • spam email prediction(spam or not-spam)
  • Here for each X feature, we have only two y values whether Yes or No, True or False, 0 or 1.

REGRESSION

  • Regression deals with continuous data.
  • For each value of X, we have the Y value changing.


  • Here X is SAT and Y is GPA, we can see how the Y value is changing for each X value we call this as continuos data
  • In regression the output values will not be the probability of occurrences like (0 or1, spam or not spam), it will be in the form of continuous data as you see in the above table

MACHINE LEARNING TREE





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