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. Data quality significantly impacts model performance. What’s the goal?

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Pickl AI

NOVEMBER 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.

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Dataconomy

MARCH 27, 2023

The goal is to create algorithms that can make predictions or decisions based on input data, without being explicitly programmed to do so. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data.

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Pickl AI

DECEMBER 3, 2024

The UCI Machine Learning Repository is a well-known online resource that houses vast Machine Learning (ML) research and applications datasets. It is a central hub for researchers, data scientists, and Machine Learning practitioners to access real-world data crucial for building, testing, and refining Machine Learning models.

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ODSC - Open Data Science

AUGUST 9, 2023

Our focus will be hands-on, with an emphasis on the practical application and understanding of essential machine learning concepts. Attendees will be introduced to a variety of machine learning algorithms, placing a spotlight on logistic regression, a potent supervised learning technique for solving binary classification problems.

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Kaggle

JULY 29, 2020

With sports (and everything else) cancelled, this data scientist decided to take on COVID-19 | A Winner’s Interview with David Mezzetti When his hobbies went on hiatus, Kaggler David Mezzetti made fighting COVID-19 his mission. Photo by Clay Banks on Unsplash Let’s learn about David!

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Dataconomy

APRIL 18, 2025

Machine learning operations (MLOps) is reshaping how organizations deploy and manage machine learning models, allowing for streamlined workflows and strong collaboration between data scientists and IT operations. Data preparation techniques: Cleaning and transforming data to make it suitable for training.

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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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