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Sunday, October 16, 2022

Machine Learning Projects with Python | Free COURSE Machine Learning course

Machine Learning Projects with Python |  Free COURSE  Machine Learning course 

DISCLOSURE: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission. 



Machine Learning Projects with Python





In this course you may construct three primary actual lifestyles gadget gaining knowledge of projects: – Project #1: House Price Prediction the use of Machine Learning In this mission, we can construct a synthetic intelligence version that predicts residence fees the use of sklearn more than one linear regression algortihm. – Project #2: Salary Calculation and the use of Machine Learning It is a tedious painting to calculate every worker’s revenue in keeping with the worker’s revel in the stage. In this mission, we're going to construct a gadget to gain knowledge of the version for the actual calculation of worker salaries. Since a maximum of revenue values are non-linear, an easy linear characteristic cannot be used for this calculation process.
 Generally, a maximum of the groups has polynomial revenue values for his or her employees.
 Therefore we can use a polynomial linear regression set of rules for an answer right here. – Project #three: Advanced Customer Segmentation the use of Machine Learning In this mission, we can use a brand new and superior segmentation library evolved through the Massachusetts Institute of Technology (MIT). The patron information in our Customer Segmentation mission, that's covered withinside the access and intermediate stage projects, changed into easy and the K-Means clustering set of rules changed into enough for segmentation.
 But lifestyles aren't that easy!
 When you've got complicated patron information, in case you do clustering with K-Means, you could get faulty results! Since the patron information on this mission is complicated information (each numeric and categorical) much like in actual lifestyles, right here we can use a unique unsupervised gaining knowledge of a set of rules in place of a popular version and divide our 2000 clients into corporations with the state-of-the-art synthetic intelligence algorithms.







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