FreeCoursesOnline Me Packt Regression Analysis for Statistics and Machine Learning in FCO

FreeCoursesOnline Me Packt Regression Analysis for Statistics and Machine Learning in FCO

Name	[FreeCoursesOnline.Me] [Packt] Regression Analysis for Statistics and Machine Learning in R [FCO]	DOWNLOAD Copy Link
Total Size	1.6 GB
Total Files	62
Last Seen	2024-10-15 23:33
Hash	46CCAD14C58FC19493E0B01EC226202B0D9CC1C0

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/1.Get Started with Practical Regression Analysis in R/
01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4	121.2 MB
02.Difference Between Statistical Analysis & Machine Learning.mp4	75.6 MB
03.Getting Started with R and R Studio.mp4	23.3 MB
04.Reading in Data with R.mp4	52.2 MB
05.Data Cleaning with R.mp4	47.0 MB
06.Some More Data Cleaning with R.mp4	30.4 MB
07.Basic Exploratory Data Analysis in R.mp4	58.3 MB
08.Conclusion to Section 1.mp4	5.6 MB
/2.Ordinary Least Square Regression Modelling/
09.OLS Regression- Theory.mp4	29.1 MB
10.OLS-Implementation.mp4	26.8 MB
11.More on Result Interpretations.mp4	18.9 MB
12.Confidence Interval-Theory.mp4	15.7 MB
13.Calculate the Confidence Interval in R.mp4	8.5 MB
14.Confidence Interval and OLS Regressions.mp4	22.3 MB
15.Linear Regression without Intercept.mp4	9.6 MB
16.Implement ANOVA on OLS Regression.mp4	7.8 MB
17.Multiple Linear Regression.mp4	18.0 MB
18.Multiple Linear regression with Interaction and Dummy Variables.mp4	31.7 MB
19.Some Basic Conditions that OLS Models Have to Fulfill.mp4	28.9 MB
20.Conclusions to Section 2.mp4	8.4 MB
/3.Deal with Multicollinearity in OLS Regression Models/
21.Identify Multicollinearity.mp4	30.1 MB
22.Doing Regression Analyses with Correlated Predictor Variables.mp4	15.0 MB
23.Principal Component Regression in R.mp4	31.0 MB
24.Partial Least Square Regression in R.mp4	20.5 MB
25.Ridge Regression in R.mp4	22.0 MB
26.LASSO Regression.mp4	13.2 MB
27.Conclusion to Section 3.mp4	6.3 MB
/4.Variable & Model Selection/
28.Why Do Any Kind of Selection.mp4	12.2 MB
29.Select the Most Suitable OLS Regression Model.mp4	40.7 MB
30.Select Model Subsets.mp4	22.1 MB
31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4	20.4 MB
32.Evaluate Regression Model Performance.mp4	41.6 MB
33.LASSO Regression for Variable Selection.mp4	9.5 MB
34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp4	26.1 MB
35.Conclusions to Section 4.mp4	4.7 MB
/5.Dealing with Other Violations of the OLS Regression Models/
36.Data Transformations.mp4	24.2 MB
37.Robust Regression-Deal with Outliers.mp4	20.0 MB
38.Dealing with Heteroscedasticity.mp4	15.6 MB
39.Conclusions to Section 5.mp4	3.6 MB
/6.Generalized Linear Models (GLMs)/
40.What are GLMs.mp4	13.3 MB
41.Logistic regression.mp4	46.6 MB
42.Logistic Regression for Binary Response Variable.mp4	33.2 MB
43.Multinomial Logistic Regression.mp4	19.1 MB
44.Regression for Count Data.mp4	16.8 MB
45.Goodness of fit testing.mp4	91.4 MB
46.Conclusions to Section 6.mp4	7.1 MB
/7.Working with Non-Parametric and Non-Linear Data/
47.Polynomial and Non-linear regression.mp4	19.8 MB
48.Generalized Additive Models (GAMs) in R.mp4	41.9 MB
49.Boosted GAM Regression.mp4	17.3 MB
50.Multivariate Adaptive Regression Splines (MARS).mp4	27.7 MB
51.CART-Regression Trees in R.mp4	29.7 MB
52.Conditional Inference Trees.mp4	12.3 MB
53.Random Forest(RF).mp4	21.5 MB
54.Gradient Boosting Regression.mp4	9.0 MB
55.ML Model Selection.mp4	107.1 MB
56.Conclusions to Section 7.mp4	26.1 MB
/Exercise Files/
code_9781838987862.zip	29.3 MB
Total files 62