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Logistic Regression, LDA and KNN in R for Predictive Modeling

Logistic Regression, LDA and KNN in R for Predictive Modeling
Logistic Regression, LDA and KNN in R for Predictive Modeling
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 5 Hours | 2.52 GB
Genre: eLearning | Language: English

You're looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in R, right? You've found the right Classification modeling course covering logistic regression, LDA and KNN in R studio!
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course. Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay if you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify model's performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a classification model in R will soar. You'll have a thorough understanding of how to use Classification modeling to create predictive models and solve business problems.
All the code and supporting files for this course are available at -

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