Digest #8 | North Pacific has 96K tonnes of Trash & Must know algorithms

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Which country is polluting our oceans? | An Infographics

Hey, This edition, I couldn’t resist sharing this cool project, put together by Jamie Kettle. This infographic shows how much plastic waste different country generates. They have also identified the percentage of plastic waste not disposed of properly.

This infographic breaks down the plastic waste in the ocean and creatively shows the amount. You can also check out a colourful bar chart that shows how well each country manages its plastic waste. The countries handling all their plastic waste properly are highlighted in bold – it's like a badge of honour!

One thing that caught my attention is that having a big economy doesn't necessarily mean a country manages its plastic waste well. There are some surprises in there, and the patterns don't always match up.

• North Pacific alone has 96,400 tonnes of trash.

• Chine produces ~60 tonnes of waste with 74% of them inadequately managed.

• While the data shows 100% of the plastic is managed well from USA and Germany, it is doubtful and probably offshored to some asian country.

Speaking about the data, I want to recommend a newsletter which I find it interesting about tools to level up the business and work life. Do check it out.

• In the Trenches w/ Romeen Sheth — Practical Frameworks, tools and tactics to level up in business and life.

Coming back to our topic, Thanks for responding to our quiz on Monday about the algorithm. We received an overwhelming response from more than 50% of our subscribers. Find a small summary explanations about the different algorithms.

K-Means is a commonly used algorithm for clustering in unsupervised machine learning. Clustering involves grouping similar data points based on certain features or attributes. K-Means works by partitioning the data into 'k' clusters, where 'k' is a user-defined parameter. It iteratively assigns data points to the nearest cluster centre (centroid) and recalculates the centroids based on the mean of the assigned data points.

Here's a brief explanation of the other options:

A) Linear Regression: Linear regression is a supervised learning algorithm used for predicting a continuous target variable based on one or more input features. It is not used for clustering.

B) Decision Tree: Decision trees are used for both classification and regression tasks in supervised learning. They are used to make decisions or predictions based on input features, but they do not perform clustering.

D) Support Vector Machine (SVM): SVM is a supervised learning algorithm used for classification and regression tasks. It aims to find a hyperplane that best separates different classes or predicts numerical values. It is not used for clustering.

In summary, K-Means (option C) is the algorithm commonly used for clustering in unsupervised machine learning. These algorithms is what makes the AI possible and lets look at few of the AI tools which will make you more productive and innovative.

AI Tools Of the Week

Clearmind- Personalised AI therapy for all.

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Languify- Make every student feel like a superhero.

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Kapwing- The easiest way to edit videos with Artificial Intelligence, from text-based video editing to generative video slide shows to automatic video transcription and subtitles, Kapwing has it all. - https://www.kapwing.com/ai

Phonesites- Easily build websites, landing pages, surveys, pop-ups, and digital business cards in just 10 minutes. - https://phonesites.com/

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