As in our previous chapters, we have discussed what is machine learning  and why Python is for machine learning. If you did not go through them, you can. It is recommended. In this chapter, we will learn about Supervised Machine learning & Unsupervised Machine Learning and their key differences.

Supervised Machine Learning

Now we shall discuss all relevant things about Supervised Machine Learning.

What is Supervised Machine Learning?

Supervised machine learning is the type of machine learning in which we teach our model, or you can say teach our computer as a model by showing it examples with labels; in that way, we are teaching our model to learn from the data, and he can make predictions based on that data and teaching that he knows from that labels now the question is what is the label?

Before going to Labels see an example what i have said above.

For Example: We are teaching our pet to identify fruits i.e Apple , Banana & mango so we show it pictures of apples, bananas, and mangos & tell it which is which. Over time, the pet learns to distinguish the fruits on its own(means identify the fruit on it owns).

Now discuss what is label ?

What is Label?

A label is a tag or identifier attached to a data point that indicates its category, class, or outcome.

For example: In a dataset or you can say container of a data we have keep record of flowers in which there are 3 category i.e “rose,” “daisy,” & “tulip.” These labels help a machine learning model learn to recognize and classify different types of flowers based on their visual characteristics. See also image of this type of example.

What are Attributes and Features?

The data includes different characteristics, like flower color, flower size, and flower shape. These characteristics are attributes, and each one is a feature that helps the model understand i hope you got it.

Classification and Regression In Supervised Machine Learning

In supervised machine learning, we have two main jobs. If we’re deciding between categories, like “flower” or “No flower,” it’s called classification. If we’re predicting a continuous value, like estimating numbers of flower in bundle, it’s regression we will further discuss it don’t worry.

UnSupervised Machine Learning

Now let’s discuss what is unsupervised machine learning. So unsupervised machine learning is that we not give computer on train our models on given labels like in supervised machine learning. We just give it data and it own itself through different set of rules and algorithms it makes pattern in data without telling it what to look for. Let also understand more with an example:

For example: Let’s consider above example like we have taken of flower where we provided that it is rose , tulip or daisy so in this case we are not going to give computer labels we give like groups of flower and ask it to identify whether the flower is rose , tulip o daisy on the given characteristics okay. See image for more clarification.

Dimension Reduction and Clustering In UnSupervised Machine Learning

Unsupervised learning includes techniques like reducing data to its most important parts (dimension reduction) and grouping similar things together (clustering) on the basis of their characteristics.

Key Differences: Supervised vs. Unsupervised Learning

There are various key differences but there are only 3 main key differences in Supervised Machine Learning & UnSupervised Machine Learning:

• Labeled vs Unlabeled Data
• Teaching vs Discovering
• Control vs Exploration

Labeled vs Unlabeled Data

In supervised learning, we give the computer labeled examples to learn from, while In unsupervised learning, the computer explores unlabeled data to find hidden patterns.

Teaching vs Discovering

Supervised learning is like teaching with clear instructions to our model. Unsupervised learning is like letting the computer be a detective, uncovering information on its own through making pattern using different set of rules and instruction.

Control vs Exploration

Supervised learning gives more control, as we guide the outcomes. Unsupervised learning is more exploratory, as the computer generates insights for us making pattern using different set of rules and instruction.

Conclusion

so concluding both types of machine learning we can say that both have their own strengths and uses. Supervised learning is great for tasks where we know the answers, while unsupervised learning is perfect for discovering new insights and patterns.

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