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 Core Terms of Machine Learning

Well hello there and welcome back!

As I promised in previous blog , I will be describing the core topics or terms you should know how to define and be smarter than you overconfident nerd friend ๐Ÿ˜‰

Model: "The part of Machine Learning that learns and makes predictions is called Model" easy right? now you know what model is! But examples? here's the example for ya'll : Neural Networks ,Random forests, etc.

Data Mining: " Digging into large amounts of data to discover hidden patterns. "

Transfer Learning:" Transferring knowledge from one task to another is called transfer learning"

 Labels: "The training set you feed to the algorithm includes desired solutions."

Supervised Learning: " The system tries to learn with the teacher." Not so technical definition ,but I am also not going into technical stuff soo early.

Unsupervised learning: " The system tries to learn without teacher." and again not so technical definition.

Association Rule Mining:" Digging into large amounts of data and discover interesting relations between attributes."

Reinforcement Learning:" system that observe the environment, select and perform actions, get rewards or penalties."

Instance based learning:" system learns examples by heart๐Ÿ˜†,meaning it generalizes to new cases by using similarity measure to compare them to learned examples."

Model-based Learning: "Build a model of the examples and then use that model to make predictions."

Feature selection:" Selecting the most useful features to train on among existing features."

Feature extraction:" Combining existing features to produce more useful one."

Overfitting:" Model performs well on training data ,but not on testing data."

Underfitting:" Model lags to learns from training data itself."

Data pipeline:" A sequence of data processing components is data pipeline."

Well this might be enough for this post. Lets learn more in next post. Starting with Types of learnings or more general coined terms in Machine learning.

HAPPY LEARNING!


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