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How to tackle lack of data: an overview on transfer learning

Data Science Blog

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

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Supercharge your skill set with 9 free machine learning courses

Data Science Dojo

Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves This is another beginner-level course that teaches you the basics of machine learning using Python. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

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How to Work Smarter, Not Harder, with Artificial Intelligence

Flipboard

Mastering machine learning techniques such as supervised, unsupervised, and reinforcement learning is key to building adaptive and effective AI systems. Effective data handling, including preprocessing, exploratory data analysis, and making sure data quality, is crucial for creating reliable AI models.

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Binary classification

Dataconomy

Binary classification is a supervised learning method designed to categorize data into one of two possible outcomes. This approach is crucial in the realms of data analysis, enabling decisions that affect real-world applications, such as healthcare, finance, and customer service. What is binary classification?

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Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

it is overwhelming to learn data science concepts and a general-purpose language like python at the same time. Exploratory Data Analysis. Exploratory data analysis is analyzing and understanding data. For exploratory data analysis use graphs and statistical parameters mean, medium, variance.

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Retell a Paper: “Self-supervised Learning in Remote Sensing: A Review”

Mlearning.ai

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., Hence it is possible to train the downstream task with a few labeled data.

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Understand The Difference Between Machine Learning and Deep Learning

Pickl AI

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. ML works with structured data, while DL processes complex, unstructured data. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two.