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Love her voice, it's so pleasant and so much clarity in explaining the concepts! I would pay to hear your voice all day lol. Thank you, finally found the best video to grasp these concepts. :)
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Hi simplilearn! I have a doubt. I would love if you help me clear it. In classification(supervised learning), we use discrete values and we classify the data to be either 1 or 0 and True or false. But can we take multiple discrete values? like we can classify it in more than two classifications? Like if we say 1 or 0 or -1? I am confused. Plz help me out with a clear explanation. I hope I am clear with my question.
"Hi , You can definitely use supervised classification algorithms to create multi-class classifier models. Please refer to this documentation by ScikitLearn to learn more scikit-learn.org/stable/modules/multiclass.html"
Thanks it was really helpful to understand what type of data I'm working on .. just to confirm . Are football datasets are unsupervised on common basis or not ?.
Hi Tapesh, if you are trying to predict the performance of a club or a team in a football dataset or if you are predicting each player's performance in a match or a group of matches, these are examples of supervised learning.
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6 лет назад
Are you going to give all machine learning tutorials on RU-vid or would be premium. Version on your website?
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No, Deep Learning is not same as Unsupervised Learning. In unsupervised learning, there is no labelled data for both independent and dependent variables. K-Means Clustering is a popular example to unsupervised learning. Deep Learning is a broader category in Machine Learning that uses neural nets to perform prediction tasks. It uses both structured and unstructured data.
In ML we don't really write precise steps for the computer like in normal programming. Instead we try to give the computers a dataset from which to learn from.That's what "not explicitly programmed" means. Hope that Helps
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unsupervised learning should have feedback mechanism as it records the history of previous customers and recommends the customer 3 to buy.Is this correct?
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"Hi Tanoy, If you are working on a supervised classification problems or unsupervised clustering problems, then you would need at least one qualitative variable. But, for a regression problem, your target variable should be quantitative."
You mentioned fraud detection as a case of unsupervised learning in first video of this series and now in this video it is mentioned as a case of supervised learning
Fraud detection in Machine Learning belongs to the category of Anomaly detection. There are 3 types of anomaly detection - supervised, unsupervised and semi-supervised anomaly detection.
Fraud detection can be carried out using both supervised and unsupervised learning algorithms. It depends on the type of fraud you are trying to detect. In supervised learning, a random sub-sample of all records is taken and manually classified as either fraudulent or non-fraudulent. Unsupervised methods don't make use of labelled records. Bolt and Hand uses Peer Group Analysis and Break Point Analysis to analyze the spending behavior in credit card accounts. Pattern recognition algorithms can be used to match fraud patterns. Supervised neural nets can also be used to learn suspicious patterns from samples.
I'm very sorry I could be a little racist to tell that but until know when I here an Indian English speaker that try to explain something I was directly closing the video because of that accent but this is the fisrt time I thought the sound is cute, clear and can totally understandable. Thank you very much for that.
You are awesome nigga , I watched about 15-20 videos for better explanation of unsupervised, lately found you.. the way you expound it just got fit into my thick skull... thanks mann... you tooo good apeksha ...
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