Hello developers, and welcome back.
In this video we're going to learn about machine learning terminology.
We're going to learn about the most used terms in ML.
Let's start with the model in simple terms, the brain or the recipe that the machine learns.
A model is the primary output of the machine learning process.
It's a mathematical function or a program that has been trained to recognize patterns in data.
Once trained, you use the model to make predictions or decisions on new, unseen data.
Think of teaching a child to recognize a dog.
You show them many pictures, the training data.
Their brain forms a pattern of what darkness looks like pointy ears, furry four legs, etc. the learned
pattern in their brain is the model.
When you later show them a picture of a dog they've never seen.
They use their mental model to correctly identify it as a dog.
In technical terms, a model can be a linear regression equation, a decision tree, a neural network,
or any other algorithm that has been fitted to data.
The second thing is feature.
In simple terms, an individual measurable property or characteristic of the data you're analyzing.
Features are the input a variable used by the model to make a prediction.
They are the columns in your data set, excluding the target.
Good features are descriptive and relevant to the problem you are trying to solve.
The process of selecting and creating good features is called feature engineering and is crucial for
model performance.
And this is for advanced topics and more and more dipping deep diving into machine learning.
For example, let's predict house prices, which is the target?
What information you would use to guess the price?
We need to consider square footage.
Number of bedrooms, number of bathrooms, zip code or the location year built.
Each of these pieces of information is a feature.
Another example email spam filter is the email spam or not spam feature the presence of certain words
like free winner, you are a winner you are.
Get your free coupon, the sender's email address, and the number of exclamation marks, etc..
Okay, so those are the features.
Another terminology is the target.
In simple terms, the answer or the output you want your model to predict.
The target is the variable you are trying to predict or explain.
It's the outcome that depends on the features.
In a data set, it's often a specific column that you ask your model to learn to predict based on all
the other columns in your data set of houses.
Let's take the house prices again in your in your data set of houses, you have columns, for example
square footage, a number of bedrooms, number of bathrooms.
Those are features.
The column sale price is the target.
So we need to get the price of this building or this this house.
So the target is the price and the sale price.
The model's goal is to learn the relationship between the features and this target price.
Again guys, this is the most important thing.
And the link between target and feature.
The model's goal is to learn the relationship between the features and this target price.
Again guys, here we are connecting the model feature and target the models.
The model's goal is to learn the relationship between the features and the target price.
So here is the relationship.
The model's goal.
The idea behind creating the model is to learn the relationship between the feature and the target.
The fourth terminology and term is training.
In simple terms, the process of teaching the model by showing it.
Examples.
Training is the core activity of machine learning.
It's the process where the model learns the relationship between the features and the target.
This is done by feeding the algorithm a large amount of data.
The training data, which includes both the features and the corrected and the correct answers, which
is the target.
The algorithm makes guesses, sees how wrong it is, and then automatically adjusts its internal parameters
to reduce its error.
Training is the process of showing the child hundreds of pictures of dogs and pictures of not dogs,
like cats and rabbits, and telling them which telling them which is which.
Each time they make a mistake, they correct their mental model.
After many examples, their model becomes accurate and well trained.
For a simple model, like a linear regression, training involves finding the line of best fit through
the data points.
The computer tries many different lines and select the one that minimizes the overall distance, which
is the error from all the known data points.
Okay, so as a quick summary feature, the input factors used for prediction target the output we want
to predict.
Training the process of learning the connection between features and target and the model.
The final trained program that makes predictions.