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This course helps you to derive meaning out of huge data with help of ML and Python

Description

Machine learning is a subfield of computer science where machines are trained to make decisions with the help of data provided without any human interference. For example, if we could teach a computer to tell if a person is lying about something, then the computer might be using machine learning as software. There are huge applications of machine learning such as Face recognition, image classification, stock market prediction, Emotion detection, self-driving cars, etc. More details about all these are covered in the training course videos. Machine learning uses knowledge from mathematics, statistics, computer science, and programming to build and deploy algorithms that can do one of those tasks mentioned above.

 

In this course on ML, we will be learning an introduction to Pima Indians Diabetes Using Machine Learning, Installation of Anaconda, Installation of Libraries, steps in Machine Learning, Dataset and Logistic Regression, Pima Classification, exclude the Header, Conversion of String into Number, Split the Dataset and Check the ROC.

 

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. This training is an introduction to the concept of machine learning and its application using Python.

 

Who this course is for:

The target audience for this course includes students and professionals who are interested in ML

This Machine learning training is also meant for people who are very keen on learning Regression and Predictive modeling

Anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers

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