پیشنهاد فرادرس

آموزش رگرسیون خطی در یادگیری ماشینی و هوش مصنوعی

دسته بندی ها: آموزش های لیندا (Lynda) ، هوش مصنوعی ، یادگیری ماشینی (Machine Learning)

رگرسیون خطی یک متد مدل سازی رابطه بین یک متغیر وابسته و یک تا چند متغیر دیگر است که می تواند به شما در حل بسیاری از مشکلات دنیای واقعی کمک کند. این دوره مهم ترین مفاهیم و تکنیک های رگرسیون خطی و نحوه استفاده از آنها را به طور موثر تشریح می کند.

سرفصل:

  • معرفی دوره
  • رگرسیون خطی ساده
  • ساخت پلات های پراکنده در Chart Builder
  • ایجاد 3D scatter plot
  • Bubble chart با GPL
  • چالش ها و مفروضات رگرسیون چندگانه
  • چک کردن مفروضات به طور بصری
  • ایجاد کدهای نمونه
  • ایجاد و تست اصطلاحات تعامل
  • درک همبستگی های جزئی
  • بررسی مشکلات و راه حل های آن
  • مدل سازی خطی خودکار
  • کار با multicollinearity
  • رگرسیون طبقه بندی با مقیاس بهینه
  • رگرسیون منطقی
  • SEM
  • و غیره
Machine Learning & AI Foundations: Linear Regression Publisher:Lynda Author:Keith McCormick Duration:3h 57m Level:Intermediate

Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.
Released: 5/30/2018
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.
Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
Building effective scatter plots in Chart Builder
Challenges and assumptions of multiple regression
Checking assumptions visually
Creating dummy codes
Creating and testing interaction terms
Understanding partial and part correlations
Spotting problems and taking corrective action
Dealing with multicollinearity
Introduction
Welcome
1m 18s
What you should know
1m 55s
Using the exercise files
1m 8s
1. Simple Linear Regression
Building effective scatter plots in Chart Builder
7m 11s
Adding labels and spikes to a scatter plot
3m 24s
Create a 3D scatter plot
2m 39s
Bubble chart with GPL
6m 14s
Residuals and R2
4m 27s
Calculating and interpreting regression coefficients
7m 23s
2. Introduction to Multiple Linear Regression
Challenges and assumptions of multiple regression
8m 5s
Checking assumptions visually
9m
Checking assumptions with Explore
9m 55s
Checking assumptions: Durbin-Watson
1m 55s
Checking assumptions: Levine's test
4m 15s
Checking assumptions: Correlation matrix
4m 31s
Checking assumptions: Residuals plot
6m 23s
Checking assumptions: Summary
3m 59s
3. Dummy Code and Interaction Terms
Creating dummy codes
8m 4s
Dummy coding with the R extension
1m 50s
Detecting variable interactions
5m 1s
Creating and testing interaction terms
4m 33s
4. Three Regression Strategies
Three regression strategies and when to use them
2m 45s
Understanding partial correlations
3m 54s
Understanding part correlations
3m 40s
Visualizing part and partial correlations
5m 11s
Simultaneous regression: Setting up the analysis
2m 43s
Simultaneous regression: Interpreting the output
7m 55s
Hierarchical regression: Setting up the analysis
5m 5s
Hierarchical regression: Interpreting the output
7m 20s
Creating a train-test partition in SPSS
4m 30s
Stepwise regression: Setting up the analysis
3m 24s
Stepwise regression: Interpreting the output
4m 5s
5. Spotting Problems and Taking Corrective Action
Collinearity diagnostics
6m 30s
Dealing with multicollinearity: Factor analysis/PCA
4m 17s
Dealing with multicollinearity: Manually combine IVs
3m 15s
Diagnosing outliers and influential points
7m 21s
Dealing with outliers: Studentized deleted residuals
5m 49s
Dealing with outliers: Should cases be removed?
6m 48s
Detecting curvilinearity
5m 20s
6. Other Approaches to Regression
Regression options
5m 20s
Automatic linear modeling
6m 37s
Regression trees
6m 19s
Time series forecasting
4m 30s
Categorical regression with optimal scaling
6m 9s
Comparing regression to Neural Nets
4m 31s
Logistic regression
4m 54s
SEM
4m 23s
Conclusion
What's next
1m 35s

پیشنهاد فرادرس

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