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Machine Learning Is when artificial intelligence (AI) helps you make decisions based on data without being programmed on something specific.

An example of this is ChatGbt you can ask it anything and it will come up with a data based response to answer what you have asked.

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In content based machine learning uses the recommendation system to make an accurate predictions and suggests things appropriate to the topic. Content-based filtering is a key approach in recommender systems that suggests items to users based on the features of items they've previously interacted with or shown interest in. Unlike collaborative filtering, which relies on user behavior patterns, content-based filtering focuses on the attributes of the items themselves.

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Collaborative filtering methods

on the similarity between items. If a user likes a particular item, the system recommends other items similar to it based on the preferences of all users. This method is often more scalable than user-based filtering for an example if you search something on Amazon is t will show things similar to the idem you searched.

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In this unit we used colab to rate 23 songs too and we learned if it was accurate to how we actually rated them. The purpose was to see if it rated it accurately to how we rated them and make decisions without explicit programming

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machine learning model is crucial to ensure it generalizes well to unseen data. This involves balancing the model's ability to fit the training data with its capacity to perform accurately on new, unseen data.

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Content-Based Filtering Models (Assignment 7.6 - Colab 4)

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Content-Based Filtering Models (Assignment 7.6 - Colab 4)

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For my experience using this was somewhat accurate. in the beginning it was getting one number off but towers the end of the rating is getting lower that I rated it or it would be super high and not similar to what I put.

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Collaborative Filtering (Assignment 7.8 - Colab 5)

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Collaborative Filtering (Assignment 7.8 - Colab 5)

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With two of my classmates we got 100% of the same rating so yes they where very accurate but with two other of my classmates we got very similar rating and they were very low percentages.

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Data ethics in machine learning (ML) is a critical field that ensures AI systems are developed and deployed responsibly, respecting human rights and societal norms

is a transformative technology shaping many aspects of our daily lives. I think it would be sample bais because it has to take samples of what you put in because so it knows what to put.