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Semi-supervised
Machine Learning
Spotle.ai Study Material
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Recap - Supervised Machine Learning
Supervised learning is a learning in which
we teach or train the machine using data
which are properly or rather correctly
labeled.
Training data
Machine learning
algorithm
Predictive model
Model
evaluation
Feedback loop
Labeled
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Recap - Unsupervised Machine Learning
Input data
Machine learning
algorithm
Outputl
Unlabeled
Unsupervised learning is the learning
of machine using information that is
neither classified nor labeled and
allowing the algorithm to act on that
information without guidance.
The biggest drawbacks
Supervised Machine Learning algorithm needs labeled data from which it will
learn. We have an enormous amount of data available in the world, including
texts, images, audios, videos, time-series and many more, but only a small
fraction such data is actually labeled. This process of labeling data, whether
algorithmically or by hand, is a very costly process, especially when we are
dealing with large volumes of data.
On the other hand, the biggest disadvantage of any Unsupervised Machine
Learning is that it’s application spectrum is limited.
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Problems! OK. How to get rid of them?
How about using the benefits of unsupervised learning and building clusters
using the dataset? Unsupervised learning doesn’t work on the labeled data. So
the cost of labeling the data is saved. And then using the benefits of supervised
learning by using a small amount of labeled dataset and classifying the dataset as
accurately as possible? We can do that. This technique is called semi-supervised
machine learning.
Supervised Learning Unsupervised Learning
Semi-supervised Machine Learning
A. Pick-up the large unlabeled dataset.
A. Label a small portion of the dataset.
A. Put the unlabeled dataset into clusters using Unsupervised Machine Learning
algorithm.
A. Build your model to use the labeled data to label and classify the rest of the
unlabeled data.
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Some applications
Internet Content Classification: There are millions and millions of webpages. It
is practically impossible to label all the webpages if you need to. Semi supervised
machine learning helps here to classify the webpages.
Audio/ Video analysis: We have an overwhelming amount of audio and video
files all over. Labeling them is a massive task, if not unfeasible. Semi supervised
learning comes handy in audio/ video analysis.
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#HappyLearning
#BeCareerReady
That’s all for today.

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Semi-supervised Machine Learning Guide

  • 2. Spotle.ai Study Material Spotle.ai/Learn Recap - Supervised Machine Learning Supervised learning is a learning in which we teach or train the machine using data which are properly or rather correctly labeled. Training data Machine learning algorithm Predictive model Model evaluation Feedback loop Labeled
  • 3. Spotle.ai Study Material Spotle.ai/Learn Recap - Unsupervised Machine Learning Input data Machine learning algorithm Outputl Unlabeled Unsupervised learning is the learning of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.
  • 4. The biggest drawbacks Supervised Machine Learning algorithm needs labeled data from which it will learn. We have an enormous amount of data available in the world, including texts, images, audios, videos, time-series and many more, but only a small fraction such data is actually labeled. This process of labeling data, whether algorithmically or by hand, is a very costly process, especially when we are dealing with large volumes of data. On the other hand, the biggest disadvantage of any Unsupervised Machine Learning is that it’s application spectrum is limited.
  • 5. Spotle.ai Study Material Spotle.ai/Learn Problems! OK. How to get rid of them? How about using the benefits of unsupervised learning and building clusters using the dataset? Unsupervised learning doesn’t work on the labeled data. So the cost of labeling the data is saved. And then using the benefits of supervised learning by using a small amount of labeled dataset and classifying the dataset as accurately as possible? We can do that. This technique is called semi-supervised machine learning. Supervised Learning Unsupervised Learning
  • 6. Semi-supervised Machine Learning A. Pick-up the large unlabeled dataset. A. Label a small portion of the dataset. A. Put the unlabeled dataset into clusters using Unsupervised Machine Learning algorithm. A. Build your model to use the labeled data to label and classify the rest of the unlabeled data.
  • 7. Spotle.ai Study Material Spotle.ai/Learn Some applications Internet Content Classification: There are millions and millions of webpages. It is practically impossible to label all the webpages if you need to. Semi supervised machine learning helps here to classify the webpages. Audio/ Video analysis: We have an overwhelming amount of audio and video files all over. Labeling them is a massive task, if not unfeasible. Semi supervised learning comes handy in audio/ video analysis.