/MachineLearning_With_Python

Machine Learning Tutorial

Primary LanguageJupyter Notebook

Index

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Machine Learning

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What is Machine Learning

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  • Machine Learning is the science (and art) of programming computers so they can learn from data.
  • Here is a slightly more general definition:

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

— Arthur Samuel, 1959

  • And a more engineering-oriented one:

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Tom Mitchell, 1997

Software Engineering Vs AI/ML

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Here the `rules` is difined as `models`

Machine Learning Application Flow

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Learning Paradigm

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To learn the parameters, we follow this paradigm

  • Collect lots of data pairs (Input Vector, Output Vector) = $\large{\color{Purple} (x, y)}$
  • Guess for the form of the hypothesis function $\large{\color{Purple} h(x; w)}$
    • Example : $\large{\color{Purple}h(x; w)= w_0+w_1x_1 + w_2x_2}$
  • For an arbitrary guess for $\large{\color{Purple}w}$
    • We will get some $\large{\color{Purple} \hat{y}= h(x; w)}$ ${\color{Blue}\textrm{which will not match the ground truth }} \large {\color{Purple} y}$
  • Define a cost function $\large{\color{Purple} J(y, \hat{y}(w))}$ depending on the difference.
  • Find optimal $\large{\color{Purple} w}$ by minimizing $\large{\color{Purple} J(w)}$ by using some optimization procedure such as Gradient Descent.

Machine Learning Algorithms Based on Trainging

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  • Machine Learning systems can be classified according to the amount and type of supervision they get during training.
  • There are four major categories:

Based on the Performence(Handling Outliers)

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