DevelopmentTrending Courses

Deep Learning Foundation : Linear Regression and Statistics

Deep Learning Foundation Linear Regression and Statistics

Deep Learning Foundation : Linear Regression and Statistics Obtain

Be taught linear regression from scratch, Statistics, R-Squared, VIF, Gradient descent, Knowledge Science Deep Learning in Python

Deep Learning Foundation Linear Regression and Statistics

What you’ll be taught
  • Arithmetic behind R-Squared, Linear Regression,VIF and extra!
  • Deep understating of Gradient descent and Optimization
  • Program your individual model of a linear regression mannequin in Python
  • Derive and resolve a linear regression mannequin, and implement it appropriately to information science issues
  • Statistical background of Linear regression and Assumptions
  • Assumptions of linear regression speculation testing
  • Writing codes for T-Take a look at, Z-Take a look at and Chi-Squared Take a look at in python
Necessities
  • Jupyter pocket book and easy python programming
Description

Hello Everybody welcome to new course which is created to sharpen your linear regression and statistical fundamentals. linear regression is start line for a knowledge science this course focus is on making your basis sturdy for deep studying and machine studying algorithms. On this course I have defined speculation testing, Unbiased estimators, Statistical take a look at , Gradient descent. Finish of the course it is possible for you to to code your individual regression algorithm from scratch.

Who this course is for:
  • Python builders interested by information science
  • information science and machine leaning engineers
Deep Learning Foundation : Linear Regression and Statistics Obtain

Direct Download   

Supply: www.udemy.com/course/linear-regression-in-python-statistics-and-coding/

The submit Deep Learning Foundation : Linear Regression and Statistics appeared first on Obtain Now.

The best way to Obtain – freedownloadcourses.xyz/how-to-download-online-courses-from-torrent-for-free/

DISCLAIMER: No Copyright Infringement Supposed, All Rights Reserved to the Precise Proprietor. This content material has been shared underneath Academic Functions Solely. For Copyright Content material Elimination Please Contact the Administrator or E-mail at Getintocourse@gmail.com

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button
x

Adblock Detected

Remove Adblock Extension to View Content - If your using one. Thank You!!!