Smart Data Collective

JUNE 4, 2021

Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Machine learning is broadly classified into three types – Supervised. In supervised learning, a variable is predicted. Semi-Supervised Learning.

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Pickl AI

NOVEMBER 13, 2024

The answer lies in the various types of Machine Learning, each with its unique approach and application. In this blog, we will explore the four primary types of Machine Learning: Supervised Learning, UnSupervised Learning, semi-Supervised Learning, and Reinforcement Learning.

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Pickl AI

NOVEMBER 28, 2024

Understanding various Machine Learning algorithms is crucial for effective problem-solving. Familiarity with cloud computing tools supports scalable model deployment. Continuous learning is essential to keep pace with advancements in Machine Learning technologies.

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Pickl AI

FEBRUARY 12, 2024

Differentiate between supervised and unsupervised learning algorithms. Supervised learning algorithms learn from labelled data, where each input is associated with a corresponding output label. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.

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AWS Machine Learning Blog

OCTOBER 30, 2024

Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval.

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Towards AI

FEBRUARY 11, 2025

Others believe that innovations in reasoning models, reinforcement learning, and self-supervised learning will continue pushing the boundaries of AI capabilities. Additionally, the computing costs associated with handling vast amounts of data remain a significant factor.

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Becoming Human

MAY 15, 2023

The two most common types of supervised learning are classification , where the algorithm predicts a categorical label, and regression , where the algorithm predicts a numerical value. Unsupervised Learning In this type of learning, the algorithm is trained on an unlabeled dataset, where no correct output is provided.

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