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Pattern Recognition in Machine Learning [Basics & Examples]

Knowledge Hut

This can be done by finding regularities in the data, such as correlations or trends, or by identifying specific features in the data. Pattern recognition is used in a wide variety of applications, including Image processing, Speech recognition, Biometrics, Medical diagnosis, and Fraud detection.

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How to get datasets for Machine Learning?

Knowledge Hut

Machine Learning without data sets will not exist because ML depends on data sets to bring out relevant insights and solve real-world problems. Machine learning uses algorithms that comb through data sets and continuously improve the machine learning model.

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Data Science Learning Path [Beginners Roadmap]

Knowledge Hut

Understanding what defines data in the modern world is the first step toward the Data Science self-learning path. There is a much broader spectrum of things out there which can be classified as data. How would one know what to sell and to which customers, based on data? This is where Data Science comes into the picture.

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What is Narrow Artificial Intelligence(Narrow AI) with Examples

Edureka

Recommendation systems: Spotify, Amazon, and Netflix use recommendation algorithms to reach audiences. After learning the user’s tastes, these algorithms recommend media, items, and music. Siri, email screening, and Netflix recommendation algorithms are examples. This method filters trash emails and categorizes them.

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Data Pipelines in the Healthcare Industry

DareData

The Challenges of Medical Data In recent times, there have been several developments in applications of machine learning to the medical industry. Odds are that your local hospital, pharmacy or medical institution's definition of being data-driven is keeping files in labelled file cabinets, as opposed to one single drawer.

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Deep Learning vs Machine Learning: What’s The Difference?

Knowledge Hut

Parameters Machine Learning (ML) Deep Learning (DL) Feature Engineering ML algorithms rely on explicit feature extraction and engineering, where human experts define relevant features for the model. DL models automatically learn features from raw data, eliminating the need for explicit feature engineering.

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What is data processing analyst?

Edureka

Organisations and businesses are flooded with enormous amounts of data in the digital era. Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation.