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Machine Learning

 

 Machine Learning:

The field of study that gives computer the ability to learn without being explicitly programmed.

 Types:

  • Supervised Learning
  • Unsupervised Learning  
  • Recommender Systems
  • Reinforcement Learning   

Supervised Learning:

    It is a method in which we give our algorithm both inputs and outputs, and the algorithm automatically generates an algorithm or mappings, so that at some point, that generated algorithm is so effective that if we give them inputs, respective outputs are automatically generated. 

The Two Main Types

  1. Regression: Predicting a number (e.g., predicting the price of a house).

  2. Classification: Predicting a category (e.g., deciding if an email is "Spam" or "Not Spam"). 

 

     Example:

 

  Here in this example, to predict the prices of houses, we plotted a graph, and we observed that we could use the function of curve to find the unknown value. But computers can't plot the graph and analyze here function of curve would be better. So, we need to figure some way to decide which function would be better here, or maybe some general function which can be applied to anything (right now i am guessing, function of derivation would be applied).

 Regression: 

 This type is called regression. In this type, we are trying to predict the number from infinitely many possible outputs.

In simple terms, regression is about predicting a specific number.

If you're trying to figure out a price, a temperature, or a score based on other data, you're doing regression.

Classification:

In classification, we usually do identification, like identifying a spam or identifying whether patient has breast cancer or not? In it the outcomes are limited (finite), not like regression, in which possible outcomes are infinite.

Key Types

  • Binary Classification: Only two choices (Yes/No, 0/1, True/False).

  • Multiclass Classification: More than two choices (Red, Blue, or Green). 

    The model looks at the data features and tries to find a boundary that separates the different groups. When you give it new data, it just checks which side of the boundary that data falls on.

    Want to see the common algorithms used for this, or should we talk about the difference between this and Regression?

     

    Example:

      

     

    SUMMARY:

      

     

Unsupervised Learning: 

A type of learning, in which we just give inputs to the model and it automatically find some meaningful information like structures, groups or meaningful outliers (exceptions).

Types:

1. Clustering

  • Definition: Automatically grouping data points that are "similar" based on their features.

  • Logic: If points are mathematically close to each other, they become a group (cluster).

  • Example: Grouping news articles by topic (Sports, Tech, Politics) without being told what the topics are.

2. Anomaly Detection

  • Definition: Identifying data points that are significantly different from the "normal" pattern.

  • Logic: The model learns what "average" looks like and flags anything that sits way outside that range.

  • Example: A bank flagging a $5,000 transaction in another country when you usually spend $20 on lunch locally.

 

 

 

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