Welcome back.
In this video, we're gonna learn about the types of ML algorithms.
And we're going to deep dive into the supervised learning.
Supervised learning is a type of machine learning where a model learns from labeled data.
Meaning every input has a corresponding correct output.
The model makes predictions and compares them with the true outputs, adjusting itself to reduce errors
and improve accuracy over time.
The goal is to make accurate predictions on new, unseen data.
For example, a model trained on images of handwritten digits can recognize new digits.
It has never been seen.
Under supervised learning for classification, regression and forecasting, classification teaches a
machine to sort things into categories.
It learns by looking at examples with labels like emails marked spam or not spam.
After learning, it can decide which category new items belong to, like identifying if a new email
is spam or not.
For example, a classification model might be trained on data set of images labeled as either dogs or
cats, and it can be used to predict the class of a new and unseen images as dogs or cats based on their
features such as color, texture, shape, tail, and others.
Okay, so the classification means sorting things into categories.
Regression.
In regression tasks, the learning machine must estimate and understand the relationships between variables
in a system by analyzing only one dependent variable, as well as a few other variables that are constantly
changing.
Regression analysis is particularly useful for forecasting and prediction.
So regression, where the goal is to predict a continuous numerical value based on one or more independent
features.
It finds relationships between variables so the prediction can be made.
We have two types of variables present.
Regression.
Dependent variable which is the target variable we are trying to predict.
For example house price or independent variable or features that the input variables are influenced
the prediction for example locality number of rooms.
So regression analysis problem works with if output variable is a real or continuous value, such as
salary or weight.
Forecasting.
Forecasting involves analyzing, analyzing past and present data to make predictions about the future.
As a quick recap about the supervised learning and the steps, the working of supervised machine learning
follows these key steps.
Training data set.
Collecting labeled data.
Gather a data set where each input has a known correct output.
For example, images of handwritten digits like three.
Like this.
Like for example this.
Those are the images of handwritten digits with their actual numbers as labels.
So the label would be number three, the label here would be number three and so on.
Okay.
So every image has its correct label.
Then splitting the data set divide the data set into training data about 80% and testing data about
20%.
And this is what we're going to do in the next videos.
The model will learn from training data.
And we evaluated on the testing data, then training the model feed the training data inputs and outputs
and their labels to a suitable supervised learning algorithm like decision trees like SVM or linear
regression.
So in the next videos, we're going to learn about the algorithms of the supervised learning.
The model tries to find patterns and that map inputs to correct outputs.
Then the fourth step is to validate and test the model.
Evaluate the model using testing data.
It has never been seen before, and the model predicts output.
And those predictions are compared with the actual labels to calculate accuracy or error.
The last step is the prediction.
Once the model performs well, it can be used to predict outputs from completely new unseen data.
Let me show you an example.
Here we have the input row data.
And here we have the labels.
Every element every image here has its own and corresponding label.
So for example this is the image of elephant elephant elephant.
And those are the elephants.
And the label class contains the elephant label.
Also this is true for the cow and camel.
So every element here, every input data has a label gathering a data set where each input has a known
correct output.
Images of animals with their actual names as label.
Then splitting the data.
Divide the data into training data about 80% for uh, 80% for training and testing data, about 20%
for training the model.
Here we have the algorithm, as I told you.
We have as a VM, we have decision trees, we have linear regression and so on.
Based on this algorithm and training data set and the desired output, we are going to use and validate
the model.
So evaluating the model using testing data the model then predicts the output.
And those predictions are compared with the actual labels to calculate accuracy or error.
This is the level of processing the data.
Then we get the output the machine.
Now recognize that this image is for elephant.
This image is for camel and this image is for cow.
Once the model performs well it can be used to predict outputs for completely new unseen data.