Showing posts with label Machine Learning Theory. Show all posts
Showing posts with label Machine Learning Theory. Show all posts
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Graduate in Physics, Master in Applied Geophysics from IIT Bombay, India. Machine Learning is my favourite topic of study and is widely used at present.
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Definition: Machine Learning is a widely used technique. Artificial Intelligence is a broad topic that consists of Machine Learning. ...
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As mentioned in the Introduction, Supervised learning is one of the algorithms of Machine Learning. Before going to discuss further, let... -
# importing modeules import numpy as np from sklearn.linear_model import LinearRegression from sklearn import linear_model import matpl... -
Clustering: Clustering is an unsupervised learning algorithm. It group data on similarity to each other. It finds simpler data sets. ...
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The linear model with more than one feature inputs are called multiple linear regression model. Suppose we have a univariate model with ...
About Me
Popular Posts
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Definition: Machine Learning is a widely used technique. Artificial Intelligence is a broad topic that consists of Machine Learning. ...
-
As mentioned in the Introduction, Supervised learning is one of the algorithms of Machine Learning. Before going to discuss further, let... -
# importing modeules import numpy as np from sklearn.linear_model import LinearRegression from sklearn import linear_model import matpl... -
Clustering: Clustering is an unsupervised learning algorithm. It group data on similarity to each other. It finds simpler data sets. ...
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The linear model with more than one feature inputs are called multiple linear regression model. Suppose we have a univariate model with ... -
Stochastic Gradient Descent: Instead of considering all the samples together, we take samples one by one. Find whether the sample is corr...
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It is an optimization technique. Optimization refers to the task of either minimizing or maximizing some function f ( x ) by altering x .... -
Evaluation of the Matrix: Evaluation of the matrices provide a key role in the accuracy of the model. There are different evaluation matr... -
The EM algorithm is used to find the (locally) maximum likelihood parameters of a statistical model in cases where the equations cannot...
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import numpy as np from sklearn import linear_model import sklearn.metrics as sm from sklearn.preprocessing import PolynomialFe...