[New!] Simple Vs. Multiple Vs. Polynomial Regression | By Dr. Ry @Stemplicity

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  • Опубліковано 20 лип 2024
  • This tutorial explains the difference between Simple Linear Regression, multiple Linear Regression and Polynomial Regression in a fun, practical and easy way!
    In this tutorial, you will learn the following:
    • Simple Linear Regression theory and intuition
    • Multiple Linear Regression theory and intuition
    • Polynomial Regression theory and intuition
    • What is the difference between Simple, Multiple and Polynomial regression?
    • When to use Simple, Multiple and Polynomial regression?
    Machine Learning is a sub-field of Artificial Intelligence that enables machines to improve at a given task with experience.
    Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years.
    Here’s a link to my new machine learning regression course on Udemy:
    www.udemy.com/machine-learnin...
    Subscribe to my channel to get the latest updates, we will be releasing new videos on weekly basis:
    / @professor-ryanahmed
    The purpose of this course is to provide students with knowledge of key aspects of machine learning regression techniques in a practical, easy and fun way. Regression is an important machine learning technique that works by predicting a continuous (dependent) variable based on multiple other independent variables. Regression strategies are widely used for stock market predictions, real estate trend analysis, and targeted marketing campaigns.
    The course provides students with practical hands-on experience in training machine learning regression models using real-world data set. This course covers several technique in a practical manner, including:
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Logistic Regression
    • Decision trees regression
    • Ridge Regression
    • Lasso Regression
    • Artificial Neural Networks for Regression analysis
    • Regression Key performance indicators
    The course is targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems.

КОМЕНТАРІ • 27

  • @maricajohnson7493
    @maricajohnson7493 3 місяці тому +2

    I thought this was the best video I’ve seen. You were precise and scholarly.

  • @telliteymurova1676
    @telliteymurova1676 Рік тому +2

    Definetly the clearest explanation

  • @frederikschutte5275
    @frederikschutte5275 Рік тому +3

    Thank you for being sharp, short and still insightful in your explanation. Really helped a lot 😊

  • @sololife9403
    @sololife9403 Рік тому +2

    wow! indeed: sharp, short, insightful, clear

  • @shavoncummings5549
    @shavoncummings5549 Рік тому +1

    Thank you!!! I spent about a day looking for a simple explanation; I've been struggling differentiating the differences, thank you for your simple examples and including the equation instead solving a complex one!

  • @geneticengineer7720
    @geneticengineer7720 3 роки тому +3

    You are doing the world a great service with such clarity. Thank you so much!!!

  • @LenaLena-lj8uj
    @LenaLena-lj8uj 2 роки тому +2

    Thank you for fast and easy explanation

  • @binduskumar3201
    @binduskumar3201 3 роки тому +2

    Simple short and to the point....great 👏👏👏

  • @uncommoncents7910
    @uncommoncents7910 2 роки тому +2

    Thank you! Excellent break down.

  • @ananixon
    @ananixon 3 роки тому +2

    Such a good explanation, thanks!

  • @davidchan6813
    @davidchan6813 3 роки тому +1

    Very clear and concise .... great help, thanks

  • @GaithTalahmeh
    @GaithTalahmeh 3 роки тому +2

    You're Awesome, Thank you!

  • @vitoriagarcia9876
    @vitoriagarcia9876 2 роки тому +2

    fantastic!

  • @erfanmoosavi9428
    @erfanmoosavi9428 Рік тому +1

    This video was so easy to learn. Thanks!

  • @nedahenareh8877
    @nedahenareh8877 3 роки тому +1

    Thank you, it was simple and understandable

  • @tanjilarehnuma4922
    @tanjilarehnuma4922 3 роки тому +2

    Well explained😍

  • @swetamasand6170
    @swetamasand6170 3 роки тому +1

    just amazing

  • @musiknation7218
    @musiknation7218 Рік тому

    Bayesian linear regression explain please

  • @alirezazareian3969
    @alirezazareian3969 3 роки тому +1

    great

  • @normanvicente1959
    @normanvicente1959 3 роки тому

    can I build a multiple polynomial regression?

  • @robindong3802
    @robindong3802 3 роки тому

    Hi, Dr. Ryan, I could not send you a message through Udemy, so forgive me I have to write you in some way.
    I just finished your ML courses, 172 sessions, but have hard time to find the code to get exam bonus, tries two codes F2@9, and F2@9&B, none of them worked. I saw many people have the same problem in Q&A.
    you are so popular now, hope this msg find you well.
    Great video, I have been following you in ML world.

  • @balachandarvenkataramani6167
    @balachandarvenkataramani6167 3 роки тому +1

    Is it possible to have multiple polynomial regression ? like : y = b0 + x1^2 + x1^3 +x2^2+x2^3 ?

    • @maximilianbatz2070
      @maximilianbatz2070 3 роки тому

      seems likely :-)

    • @raghadalqobali3590
      @raghadalqobali3590 2 роки тому

      i have a homework with multiple polynomial regression and i get struggled with it if you know something please helpppp meee!!! :(

  • @rawconsultancy7467
    @rawconsultancy7467 3 роки тому +1

    Very well explained

  • @martharajkumar7995
    @martharajkumar7995 3 роки тому +1

    why can't google recommend such videos?

  • @Thriver21
    @Thriver21 Рік тому

    But Increasing order in polynomial regression leads to overfiting