Understanding and Applying Logistic Regression
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2.5 Hours | 250 MB
Genre: eLearning | Language: English
This course will teach you both the theory and implementation of logistic regression, in Excel (using solver), Python, and R.
Logistic Regression is a great tool for two common applications: binary classification, and attributing cause-effect relationships where the response is a categorical variable. While the first links logistic regression to other classification algorithms (such as Naive Bayes), the second is a natural extension of Linear Regression. In this course, Understanding and Applying Logistic Regression, you'll get a better understanding of logistic regression and how to apply it. First, you'll discover applications of logistic regression and how logistic regression is linked to linear regression and machine learning. Next, you'll explore the s-curve and its standard mathematical form. Finally, you'll learn whether Google's stock returns will go up or down, using Excel (solver), R, and Python. By the end of this course, you'll have a strong applied knowledge of logistic regression that will help you solve complex business problems.
(Buy Long-term Premium Accounts To Support Me & Max Speed)
Uploadgig.com (50GB Daily) :
Rapidgator (upto 12TB Traffic):
Nitroflare (25GB Daily) :