Machine Learning
We can use unsupervised learning for data analysis. Some of the main uses are as follows:
Clustering of data: We can use unsupervised learning to find clusters in data.
Features of data: We can find low-dimensional representations and features of data by using unsupervised learning.
Interesting directions and links in data: We can determine interesting directions as well as links in data by unsupervised learning.
Find relationships: We can use unsupervised learning to find relationships and correlation in data.
Outliers: We can use unsupervised learning to identify outliers or novel observations in data.
Clustering of data: We can use unsupervised learning to find clusters in data.
Features of data: We can find low-dimensional representations and features of data by using unsupervised learning.
Interesting directions and links in data: We can determine interesting directions as well as links in data by unsupervised learning.
Find relationships: We can use unsupervised learning to find relationships and correlation in data.
Outliers: We can use unsupervised learning to identify outliers or novel observations in data.
Some of the popular uses of supervised learning in ML are as follows:
Classification: We can use supervised learning to determine classes in data. We can predict the class of a data point based on the features of data.
Speech recognition: Supervised learning is used in speech recognition to understand human speech by computers. This is also used in translating speech to text as well as in translating text from one language to another language.
Regression: In Regression, we can determine the value of a dependent variable based on the values of independent variables. Eg. Finding the price of a house based on its sq. ft. and other features by using linear regression.
Marketing: We can use supervised learning to find the lifetime value of a customer in customer analytics.
Classification: We can use supervised learning to determine classes in data. We can predict the class of a data point based on the features of data.
Speech recognition: Supervised learning is used in speech recognition to understand human speech by computers. This is also used in translating speech to text as well as in translating text from one language to another language.
Regression: In Regression, we can determine the value of a dependent variable based on the values of independent variables. Eg. Finding the price of a house based on its sq. ft. and other features by using linear regression.
Marketing: We can use supervised learning to find the lifetime value of a customer in customer analytics.
Naive Bayes (NB) algorithm is a classification technique of machine learning. It is based on Bayes’ theorem. We use it for classifying the input data into distinct classes.
As per the NB algorithm, the classifier assumes that all the features used for prediction are independent of each other.
E.g. fruit can be predicted as an orange based on its weight, height, and color. NB algorithm does not try to find any relationship between these features. It uses all the three features independently to determine that this fruit is orange.
NB algorithm is very easy to build. It is very useful for large data sets.
NB is considered one of the most significant and reliable algorithms of classification.
As per the NB algorithm, the classifier assumes that all the features used for prediction are independent of each other.
E.g. fruit can be predicted as an orange based on its weight, height, and color. NB algorithm does not try to find any relationship between these features. It uses all the three features independently to determine that this fruit is orange.
NB algorithm is very easy to build. It is very useful for large data sets.
NB is considered one of the most significant and reliable algorithms of classification.
Some of the advantages of Naive Bayes (NB) classifier are as follows:
Simple: NB is very simple to implement. It just needs calculation of the probability of each feature and then it uses all these probabilities to determine the class of a data point.
Large Dataset: NB can be used on very large data sets. Since NB is simple to use, it requires very little computation power compared to other complex algorithms.
Performance: Compared to other complex models, NB provides a very reliable classification.
Training Data: NB requires less training data compared to other algorithms.
Simple: NB is very simple to implement. It just needs calculation of the probability of each feature and then it uses all these probabilities to determine the class of a data point.
Large Dataset: NB can be used on very large data sets. Since NB is simple to use, it requires very little computation power compared to other complex algorithms.
Performance: Compared to other complex models, NB provides a very reliable classification.
Training Data: NB requires less training data compared to other algorithms.
We can use Pattern recognition in the following areas:
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- Data mining
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- Speech recognition
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- Statistical analysis
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- Computer vision
- Bio-informatics
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