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Core Machine Learning Course: Regression Analysis ๐Ÿค–

Course Overview ๐Ÿ“š

Welcome to the Core Machine Learning Course focused on Regression Analysis! ๐ŸŽ‰ This course aims to build a strong foundation in machine learning, providing in-depth explanations of regression analysis, which is often overlooked in many courses.

What Will You Learn? ๐Ÿง 

  • Setting up the foundation for machine learning, including understanding learning, generalization, definitions, and types with practical examples. ๐Ÿ› ๏ธ
  • Comprehensive coverage of regression analysis, starting from basic concepts and going all the way to advanced techniques. ๐Ÿ“ˆ
  • Hands-on projects that demonstrate the application of regression analysis in real-world scenarios. ๐Ÿ’ผ

Course Benefits ๐ŸŽ

  • Free course updates to keep you up-to-date with the latest developments in machine learning and regression analysis. ๐Ÿ”„
  • Regular assignments to help you practice and enhance your skills. ๐Ÿ“
  • A curated learning plan for beginners to complete the course in just one month, along with required reading materials. ๐Ÿ—“๏ธ

Getting Started ๐Ÿš€

To get the most out of this course, sign up for free via our Learning Management System (LMS) by following this link. By signing up, you will receive free updates, have your assignments checked, and gain access to the carefully crafted learning plan. ๐Ÿ–Š๏ธ

All the materials is uploaded in the lms!

Course Content ๐Ÿ“‹

The course is divided into several sections, covering topics such as:

  • Introduction to Machine Learning ๐ŸŒŸ
  • Regression Analysis Basics ๐Ÿ“Š
  • Linear Regression ๐Ÿ”ข
  • Multiple Regression ๐Ÿ“š
  • Polynomial Regression ๐Ÿงฎ
  • Advance Regression Analysis ๐Ÿ“
  • Feature Engineering ๐Ÿ—๏ธ
  • Model Evaluation and Selection ๐Ÿ†
  • Hands-on Projects ๐Ÿ› ๏ธ

Contributing ๐Ÿ’ก

If you have any suggestions or find any issues in the course materials, please feel free to create an issue or submit a pull request. ๐Ÿ™Œ

License ๐Ÿ“„

This course and all its materials, including the lecture notes, code, are strictly copyrighted. Unauthorized distribution, reproduction, or usage of any materials from this course is strictly prohibited. All rights reserved.

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