# آموزش رگرسیون خطی و منطقی در یادگیری ماشینی

Machine Learning - Linear and Logistic RegressionPublisher: TotalTrainingAuthor: LoonycornDuration: 04:50:47Link:https://www.safaribooksonline.com/library/view/machine-learning-/10000LCLLREGR/Build robust models in Excel, R, and Python! This Linear and Logistic Regression online training course will teach you how to build robust linear models and do logistic regressions that ... - Selection from Machine Learning - Linear and Logistic Regression [Video]
Release Date: June 2017
ISBN: 10000LCLLREGR
Video Description
Build robust models in Excel, R, and Python! This Linear and Logistic Regression online training course will teach you how to build robust linear models and do logistic regressions that will stand up to scrutiny when you apply them to real world situations. You will learn about topics such as: understanding random variables, cause-effect relationships, maximum likelihood estimation, and so much more. Follow along with the experts as they break down these concepts in easy-to-understand lessons. Supplemental Materials included!
INTRODUCTION
You, This Course, and Us
00:01:55
CONNECT THE DOTS WITH LINEAR REGRESSION
Using Linear Regression to Connect the Dots
00:09:04
Two Common Applications of Regression
00:05:24
Extending Linear Regression to Fit Non-linear Relationships
00:02:36
BASIC STATISTICS USED FOR REGRESSION
Understanding Mean and Variance
00:06:04
Understanding Random Variables
00:16:55
The Normal Distribution
00:09:32
SIMPLE REGRESSION
Setting up a Regression Problem
00:11:37
Using Simple Regression to Explain Cause-Effect Relationships
00:04:57
Using Simple Regression for Explaining Variance
00:08:07
Using Simple Regression for Prediction
00:04:04
Interpreting the Results of a Regression
00:07:26
Mitigating Risks in Simple Regression
00:07:57
APPLYING SIMPLE REGRESSION
Applying Simple Regression in Excel
00:11:57
Applying Simple Regression in R
00:11:14
Applying Simple Regression in Python
00:06:06
MULTIPLE REGRESSION
Introducing Multiple Regression
00:07:04
Some Risks Inherent to Multiple Regression
00:10:06
Benefits of Multiple Regression
00:03:49
Introducing Categorical Variables
00:06:58
Interpreting Regression results - Adjusted R-squared
00:07:02
Interpreting Regression results - Standard Errors of Coefficients
00:08:12
Interpreting Regression results - T-Statistics and P-Values
00:05:33
Interpreting Regression results - F-Statistic
00:02:52
APPLYING MULTIPLE REGRESSION USING EXCEL
Implementing Multiple Regression in Excel
00:08:54
Implementing Multiple Regression in R
00:06:26
Implementing Multiple Regression in Python
00:04:21
LOGISTIC REGRESSION FOR CATEGORICAL DEPENDENT VARIABLES
Understanding the Need for Logistic Regression
00:09:24
Setting up a Logistic Regression Problem
00:06:03
Applications of Logistic Regression
00:09:55
The Link between Linear and Logistic Regression
00:08:13
The Link between Logistic Regression and Machine Learning
00:04:16
SOLVING LOGISTIC REGRESSION
Understanding the Intuition behind Logistic Regression and the S-curve
00:06:21
Solving Logistic Regression using Maximum Likelihood Estimation
00:10:03
Solving Logistic Regression using Linear Regression
00:05:32
Binomial vs Multinomial Logistic Regression
00:05:21
APPLYING LOGISTIC REGRESSION
Predict Stock Price Movements using Logistic Regression in Excel
00:09:53
Predict Stock Price Movements using Logistic Regression in R
00:08:00
Predict Stock Price Movements using Rule-based and Linear Regression
00:06:44
Predict Stock Price Movements using Logistic Regression in Python
00:04:50

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