Dataconomy

APRIL 16, 2025

Supervised learning is a powerful approach within the expansive field of machine learning that relies on labeled data to teach algorithms how to make predictions. What is supervised learning? Supervised learning refers to a subset of machine learning techniques where algorithms learn from labeled datasets.

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Dataconomy

MARCH 12, 2025

Support Vector Machines (SVM) are a type of supervised learning algorithm designed for classification and regression tasks. This decision boundary is crucial for achieving accurate predictions and effectively dividing data points into categories. What are Support Vector Machines (SVM)?

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How to Learn Machine Learning

MARCH 25, 2025

At the core of machine learning, two primary learning techniques drive these innovations. These are known as supervised learning and unsupervised learning. Supervised learning and unsupervised learning differ in how they process data and extract insights.

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Smart Data Collective

JUNE 7, 2019

For centuries before the existence of computers, humans have imagined intelligent machines that were capable of making decisions autonomously. At the early era of Artificial Intelligence, programmers tried to teach machines from the definition of logical rules that the machine itself could extend during the execution of the program.

Machine Learning 101

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Towards AI

FEBRUARY 20, 2024

I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’. Photo by Andrea De Santis on Unsplash So, What is Machine Learning? Definition says, machine learning is the ability of computers to learn without explicit programming.

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Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Lets delve into its mathematical definition and key properties.

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Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, data preparation, and algorithm selection. Ethical considerations are crucial in developing fair Machine Learning solutions.

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