#SupervisedMachineLearning |
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Supervised learning is where you have input variables (x) and an output variable (Y), and you use an algorithm to learn the mapping function from the input to the output.
The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.
In these tutorials, you will learn the basics of Supervised Machine Learning, Linear Regression and more.

About the Speaker: Mukesh Rao

Mukesh is an Adjunct Faculty at Great Lakes for Big Data and Machine Learning. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science.

Mukesh has conducted over 100 corporate trainings. Data Science training covers all the stages of CRISP-DM, tools, and techniques used in each stage, machine learning algorithms and their application. Big Data training covers core Apache Hadoop technologies including HDFS, YARN, Map Reduce, PIG, HIVE, SQOOP, FLUME, SPARK, and MongoDB.

#MachineLearningTutorial #LinearRegression #GreatLakes #GreatLearning #Tutorial

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