Machine Learning - Algorithms and Applications

Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that allow computers to perform specific tasks without explicit instructions. Machine learning is used in areas such as data analysis, pattern recognition, and predictive modeling.
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Technology
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Utilized in various industries for data analysis and prediction
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Prepared by Shruti Sahoo, reviewed by Dr. Eugene Smith

Machine Learning FAQ


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What is the difference between AI and machine learning?

While AI refers to the general attempt to create machines capable of human-like cognitive abilities, machine learning specifically refers to the use of algorithms and data sets to do so. Machine learning is typically the most mainstream type of AI technology in use around the world today.

What is the difference between machine learning and data mining?

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

What is supervised learning in machine learning?

Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately.

What are the different types of machine learning?

Machine learning can be broadly classified into three types based on the nature of the learning system and the data available: supervised learning, unsupervised learning, and reinforcement learning. Let's delve into each of these: Supervised learning is the most common type of machine learning.

Machine Learning References

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