MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 546 MB | Duration: 2h 6m
What you'll learn
Confusion Matrix and calculating various parameers from confusion matrix.
Requirements
Basics of Machine Learning. Student should be conversant with basics of machine learning and classification techniques or he/she should be a student of machine learning.
Description
This Course basically aims to help the newbees and novice programmers to machine learning make understanding of Confusion Matrix Simple. Since Confusion Matrix as the name suggests itself is very confusing for the newbees of Machine Learning to understand, this course makes an attempt to explain each and every concept of Confusion matrix in a slow and simple manner by presenting relevant examples as and when required. There are various terms like True positive, true negative, false positive, false negative , actual positive, actual negative, predicted positive, predicted negative and many others to name a few. These terminologies and their placement within the confusion matrix itself is very tricky. An attempt is made through this course to help make the understanding of these terminologies crystal clear so that you would be confident enough to tackle problems related to Confusion matrix. It will make your interpretation of Confusion matrix very easy. The course also focusses on various permutations and combinations and shuffling of the rows and columns of confusion matrix to ensure you dont get baffeled by such shufflings. The course helps you with the interpretation of the confusion matrix and helps you understand the meaning of various parameters like Sensitivity, specificity etc. which help you in finding the efficacy of the classification model under consideration using the confusion matrix.
Who this course is for:
Students who are learning machine learning and who find confusion matrix a bit confusing
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