Source: cloudskillsboost.google.com

<aside> <img src="/icons/snowflake_purple.svg" alt="/icons/snowflake_purple.svg" width="40px" /> AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence.

Machine Learning gives computers the ability to learn without explicit programming.

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Source: cloudskillsboost.google.com

The main difference between unsupervised and supervised machine learning models lies in the type of learning they perform:

1. Supervised Learning:
   • Nature of Data: Requires labeled training data, where each input is paired with the corresponding correct output.
   • Objective: The model learns to map input features to the correct output by finding patterns and relationships in the labeled data.
   • Examples: Classification and Regression problems, where the algorithm predicts a target variable based on input features.
2. Unsupervised Learning:
   • Nature of Data: Involves unlabeled data, where the algorithm explores the data structure and identifies patterns without explicit output labels.
   • Objective: The model discovers inherent structures, relationships, or groupings within the data without predefined target variables.
   • Examples: Clustering (grouping similar data points), Dimensionality Reduction (simplifying the data while retaining its essential features).

In summary, supervised learning deals with labeled data for training, aiming to predict specific outcomes, while unsupervised learning works with unlabeled data to uncover patterns and structures inherent in the data itself.

Source: cloudskillsboost.google.com

For instance, let’s say we are the owner of restaurant. We had historical data of the bill amount and how much different people tipped based on order type. Whether it was picked up or delivered. In supervised learning the model learns from past value which in this case is tip.

• So, here the model uses the total bill amount to predict the future tip amount based on whether an order was picked up or delivered.

Source: cloudskillsboost.google.com

Look at tenure and income data then group or cluster employees. To see whether someone is on the fast track. Unsupervised problems are all about discovery, about looking at the raw data and seeing if it naturally falls into groups.

Understanding concept of generative AI

Source: cloudskillsboost.google.com

In supervised learning, testing data values or x are input into the model. The model outputs a prediction and compares that prediction to the training data used to train the model.