Udemy - Machine Learning Basics - Practical Linear Regression in R

[ FreeCourseWeb.com ] Machine Learning Basics: Practical Linear Regression in R



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Created by Kate Alison | Last updated 1/2021

Duration: 2 hours | 4 sections | 22 lectures | Video: 1280x720, 44 KHz | 745 MB

Genre: eLearning | Language: English + Sub

Learn Practical Linear Regression in R - Basics of machine learning, deep learning, statistics & Artificial Intellegence



What you'll learn

Analyse and visualize data using Linear Regression

Learn different types of linear regressions (1-dimensional and multi-dimensional models, logistic regressions, ANOVA, etc)

Learn how to interpret and explain machine learning models

Plot the graph of results of Linear Regression to visually analyze the results

Assumptions of linear regression hypothesis testing

Do feature selection and transformations to fine tune machine learning models

Fully understand the basics of Machine Learning & Linear Regression Models from theory to practice

Learn how to deal with the categorical data in your regression modeling and correlation between variables

Learn the basics of R-programming



Requirements

Availabiliy computer and internet & strong interest in the topic

Description

Practical Linear Regression in R - Hands-On

This course teaches you about the most common & popular technique used in Data Science & Machine Learning: Linear Regression. You will learn the theory as well as applications of different types of linear regression models. At the end of the course, you will completely understand and know how to apply & implement in R linear models, how to run model's diagnostics, and how to know if the model is the best fit for your data, how to check the model's performance and to make predictions.

Linear regression is the simplest machine learning (and thus deep learning) model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:

machine learning

deep learning

data science

statistics



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