This question may help a lot of people to be able to decide on what to use, when, and why.
Also what I mean by the nature of data is beyond the type of the dataset itself ( Time series, images, speech etc ...)
I'll make sure to synthesize all the answers in one place.
Dear Ihababdelbasset Annaki
K-means clustering is a basic and widely used unsupervised machine learning technique.
Predicting future patterns in pricing, sales, and stock trading are the most typical applications of supervised learning. Linear Regression, Logistical Regression, Neural Networks, Decision Trees, Random Forest, Support Vector Machines (SVM), and Naive Bayes are examples of supervised algorithms.
Kind Regards
Qamar Ul Islam
Check out the attachment for a great introduction. Best wishes David Booth
Machine learning for image classification involves recognition of patterns (pattern recognition) from images from the perspective of computers (computer vision) and utilizing the patterns to classify specific domains of images (e.g. medical imaging) based on similarities (e.g. disease or normal). In this way machines can be used to do the work of domain experts (e.g. doctors).
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