3. Introduction
Machine learning is the study of algorithms that improve
their performance, at some task, with experience (TOM
MITCHELL)
� Economy
� Medical
� Gaming
� Human behaviour
� Load Forecasting
� Industry
� Climate Change
� Autonomous Cars
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7. Supervised Learning
Supervised learning is the machine learning task of learning a function that
maps an input to an output based on example input-output pairs.
•Train The Model
•Test The model
•Accuracy ( Classification_report,…)
•Loss(MSE, MAE, …)
•-Repeat
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13. Data Mining
Preprocessing:
1. Analysis the data
2. Using Training, Test
3. Normalize
Prediction:
1. Overfitting and Underfitting Problems
2. Accuracy
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16. Unsupervised Learning
Unsupervised learning is a type of machine learning that looks for previously
undetected patterns in a data set with no pre-existing labels and with a
minimum of human supervision.
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18. K-MEANS
k-means clustering is a method, that aims to partition n observations into k
clusters in which each observation belongs to the cluster with the nearest mean
(cluster centers or cluster centroid), serving as a prototype of the cluster.
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21. Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning concerned with how
software agents ought to take actions in an environment in order to maximize
the notion of cumulative reward.
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22. Deep Learning
Deep learning is an artificial intelligence function that imitates the workings of
the human brain in processing data and creating patterns for use in decision
making.
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