Machine Learning | Semi-Supervised Learning

Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning and supervised learning. It is a special instance of weak supervision. #SSL #DataScience #MachineLearning
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[@msktheabbasi4818]

Thank u sir👍

[@mabasadailycode1781]

As usual, thank you for such clear explanations sir🙌. If possible I would greatly appreciate it if you are in a position to help me understand these kinds of scenarios: Assuming I have 10000 labeled images then 1000 unlabeled images, SSL would it be an ideal approach or just go with supervised learning since the labeled samples seem to suffice for training a classifier? Also in a multi-classification problem what if I have a lot of unlabeled data that I believe they are related to some particular classes and not have any possibility of being related to other individual classes from the labeled data. For instance I have 3 classes Dogs, Cats and horses but I have a lot of unlabeled data related to only cats and dogs. Won’t this imbalance have a negative effect in training my classifier?

[@mabasadailycode1781]

As usual, thank you for such clear explanations sir. If possible I would greatly appreciate it if you are in a position to help me understand these kinds of scenarios: Assuming I have 10000 labeled images then 1000 unlabeled images, SSL would it be an ideal approach or just go with supervised learning since the labeled samples seem to suffice for training a classifier? Also in a multi-classification problem what if I have a lot of unlabeled data that I believe they are related to some particular classes and not have any possibility of being related to other individual classes from the labeled data. For instance I have 3 classes Dogs, Cats and horses but I have a lot of unlabeled data related to only cats and dogs. Won’t this imbalance have a negative effect in training my classifier?

[@peddarapuchandrasekhar8230]

Sir what is label data clarify my dobt

[@mabasadailycode1781]

As usual, thank you for such clear explanations sir🙌. If possible I would greatly appreciate it if you are in a position to help me understand these kinds of scenarios: Assuming I have 10000 labeled images then 1000 unlabeled images, SSL would it be an ideal approach or just go with supervised learning since the labeled samples seem to suffice for training a classifier? Also in a multi-classification problem what if I have a lot of unlabeled data that I believe they are related to some particular classes and not have any possibility of being related to other individual classes from the labeled data. For instance I have 3 classes Dogs, Cats and horses but I have a lot of unlabeled data related to only cats and dogs. Won’t this imbalance have a negative effect in training my classifier?