User log in
User registration
Registrations are open
Click here to register
Email: forgotten email?
Password: lost pass?
  Resend activation email
Get VPN Now for FREE! Get VPN Now for FREE! Get VPN Now for FREE! Get VPN Now for FREE! Protect your privacy! Use a VPN When Downloading Torrents
Your IP Address is . Location is .
Your Internet Provider is tracking your torrent activity! Hide your IP ADDRESS with a VPN!
We strongly recommend using Trust.Zone VPN to anonymize your torrenting. It's FREE!
Also, while you are using Trust.Zone VPN you get No Ads for Demonoid members Get VPN Now for FREE!
Details for PacktPub | Understanding Regression Techniques [Video] [FCO]
Created by Ratio: 1.00SaM 1 year ago
Lynda and other Courses >>>
For Developer Tools & Apps >>>
Forum for discussion >>>

By: Najib Mozahem
Released: March 24, 2020
Course Source:

Explore the fundamentals of linear regression, logistic regression, and count model regression in an intuitive and non-mathematical way

Video Details

ISBN 9781800200197
Course Length 7 hours 10 minutes


• Understand the concept of regression
• Build logistic regression models
• Interpret regression results
• Build linear regression models
• Build count models
• Visualize the results


Linear and logistic regressions are among the first set of algorithms you’ll study to get started on your journey in data science.

This course explores three basic regressions—linear, logistic, and count model. Starting with linear regressions, you’ll first understand the difference between simple and multiple linear regressions and explore different types of variables, including binary, categorical, and quadratic. Once you’ve got to grips with the fundamentals, you’ll apply what you’ve learned to solve a case study. As you advance, you’ll explore logistic regression models and cover variables, non-linearity tests, prediction, and model fit. Finally, you’ll get well-versed with count model regression.

By the end of the course, you’ll be equipped with the knowledge you need to investigate correlations between multiple variables using regression models.

All the codes and supporting files for this course will be available at-


• Understand the normality and independence of residuals
• Explore both graphical and non-graphical tests for non-linearity in logistic regression models
• Get to grips with count tables, their risk, and incidence rate ratio


Najib Mozahem

Najib Mozahem works as a researcher and as an assistant professor at the university level, where he teaches Quantitative Analysis. He holds a Bachelor’s degree in Computer and Communication Engineering, completed his MBA with distinction, and completed his Ph.D. in Organizational Theory where he won the best thesis prize for Ph.D. He has also received the teaching excellence award for the year 2016 – 2017. His research interests include quantitative modeling and the study of human behavior in organizations.

Facebook Twitter Digg Reddit LinkedIn StumbleUpon Email
Show Demonoid some love with BitCoin: 1DNoidyJgB159bLJT5hDnCkZ4uQrhkfBVk How to get BitCoins?
Peers: 2 seeders, 1 leechers, 795 total - Updated: 6 months ago [ Update now ]
Size: 3.09 GB  

Sponsored links
Related torrents
Torrents you may also like:
Download this torrent
Extra information
Tracker: udp://
Torrent hash: CB43C016 839B9D70 BC771FDF DA702F2F A61D88CE
Files described inside the torrent: 95 [ Click here to show the full list
No comments posted yet

Disclaimer: None of the files shown here are actually hosted or transmitted by this server. The links are provided solely by this site's users. The site moderation is also a service provided by the site's users. The administrator of this site ( cannot be held responsible for what its users post, or any other actions of its users. You may not use this site to distribute or download any material when you do not have the legal rights to do so. It is your own responsibility to adhere to these terms.

By using this site you indicate your agreement to our terms and conditions
Link to us | Contact us | API | Commemoration | Feeds/RSS | DMCA
This site and the Demonoid logo are Copyright © Demonoid. All rights reserved.

Show Demonoid some love with 1DNoidyJgB159bLJT5hDnCkZ4uQrhkfBVk