Udemy - Regression Analysis in R for Machine Learning & Data Science

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.5 GB
  • Uploaded BynotmrME
  • Downloads68
  • Last checkedJun. 19th '21
  • Date uploadedJun. 15th '21
  • Seeders 3
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Regression Analysis in R for Machine Learning & Data Science
Learn Complete Hands-On Regression Analysis in R for Machine Learning, Statistical Analysis, Data Science, Deep Learning



This course includes:
* 4.5 hours on-demand video




What you'll learn
* Your comprehensive guide to Regression Analysis & supervised machine learning using R-programming language
* Graphically representing data in R before and after analysis
* It covers the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language in R-Studio
* Implement Ordinary Least Square (or simple linear) regression, Random FOrest Regression, Decision Trees, Logistic regression and others using R
* Perform model's variable selection and assess regression model's accuracy
* Build machine learning based regression models and test their performance in R
* Compare different different machine learning models for regression tasks in R
* Learn how to select the best statistical & machine learning model for your task
* Learn when and how machine learning models should be applied
* Carry out coding exercises & your independent project assignment


Regression Analysis for Machine Learning & Data Science in R

My course will be your hands-on guide to the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language.

Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY REGRESSION ANALYSIS (Linear Regression, Random Forest, KNN, etc) in R (many R packages incl. caret package will be covered) for supervised machine learning and prediction tasks.

This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (i.e. regression analysis). Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain.

THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTISE

Fully understand the basics of Regression Analysis (parametric & non-parametric methods) & supervised Machine Learning from theory to practice

Harness applications of parametric and non-parametric regressions in R

Learn how to apply correctly regression models and test them in R

Learn how to select the best statistical & machine learning model for your task

Carry out coding exercises & your independent project assignment

Learn the basics of R-programming

Get a copy of all scripts used in the course

and MORE

NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:

You’ll start by absorbing the most valuable Regression Analysis & R-programming basics, and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using real data obtained from different sources. Thus, after completing my Regression Analysis for Machine Learning in R course, you’ll easily use different data streams and data science packages to work with real data in R.

In case it is your first encounter with R, don’t worry, my course a full introduction to the R & R-programming in this course.

This course is different from other training resources. Each lecture seeks to enhance your Regression modeling and Machine Learning skills in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of cutting edge data science methods.

The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.

One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R tools.

JOIN MY COURSE NOW!

Files:

Regression Analysis in R for Machine Learning & Data Science
  • Downloaded from 1337x.txt (0.0 KB)
    • 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more
    • 005 Lab_ Machine Learning Models' Comparison & Best Model Selection.mp4 (101.2 MB)
    • 001 Classification and Decision Trees (CART)_ Theory.mp4 (13.3 MB)
    • 002 Lab_ Decision Trees in R.mp4 (52.0 MB)
    • 003 Random Forest_ Theory.mp4 (21.3 MB)
    • 004 Lab_ Random Forest in R.mp4 (100.1 MB)
    • 006 Your Final Project.mp4 (15.0 MB)
    • 044 025_DecisionTress.R (1.2 KB)
    • 046 026_RandomForest.R (1.8 KB)
    • 047 027_ModelCompare.R (3.1 KB)
    01 Introduction to the course, Machine Learning & Regression Analysis
    • 002 Introduction to Regression Analysis.mp4 (49.1 MB)
    • 003 What is Machine Leraning and it's main types_.mp4 (34.3 MB)
    • 004 Overview of Machine Leraning in R.mp4 (5.7 MB)
    • 001 Introduction.mp4 (21.4 MB)
    02 Software used in this course R-Studio and Introduction to R
    • 001 Introduction to Section 2.mp4 (3.8 MB)
    • 002 What is R and RStudio_.mp4 (12.2 MB)
    • 003 How to install R and RStudio in 2020.mp4 (16.7 MB)
    • 004 Lab_ Install R and RStudio in 2020.mp4 (38.7 MB)
    • 005 Introduction to RStudio Interface.mp4 (30.7 MB)
    • 006 Lab_ Get started with R in RStudio.mp4 (47.7 MB)
    03 R Crash Course - get started with R-programming in R-Studio
    • 001 Introduction to Section 3.mp4 (4.0 MB)
    • 002 Lab_ Installing Packages and Package Management in R.mp4 (24.1 MB)
    • 003 Variables in R and assigning Variables in R.mp4 (9.0 MB)
    • 004 Lab_ Variables in R and assigning Variables in R.mp4 (7.6 MB)
    • 005 Overview of data types and data structures in R.mp4 (27.2 MB)
    • 006 Lab_ data types and data structures in R.mp4 (48.1 MB)
    • 007 Vectors' operations in R.mp4 (35.9 MB)
    • 008 Data types and data structures_ Factors.mp4 (9.3 MB)
    • 009 Dataframes_ overview.mp4 (16.7 MB)
    • 010 Functions in R - overview.mp4 (24.8 MB)
    • 011 Lab_ For Loops in R.mp4 (24.8 MB)
    • 011 R Crash Course I_udemy_script.R (12.9 KB)
    • 012 Read Data into R.mp4 (31.9 MB)
    04 Linear Regression Analysis for Supervised Machine Learning in R
    • 001 Overview of Regression Analysis.mp4 (49.2 MB)
    • 002 Graphical Analysis of Regression Models.mp4 (16.1 MB)
    • 003 Your first linear regression model in R.mp4 (53.3 MB)
    • 004 Lab_ Correlation & Linear Regression Analysis in R.mp4 (13.1 MB)
    • 005 How to know if the model is best fit for your data - theory.mp4 (9.1 MB)
    • 006 Lab_ Linear Regression Diagnostics.mp4 (43.2 MB)
    • 007 Lab how to measure the linear model's fit_ AIC and BIC.mp4 (8.6 MB)
    • 008 Evaluation of Prediction Model Performance in Supervised Learning_ Regression.mp4 (6.7 MB)
    • 009 Predict with linear regression model & RMSE as in-sample error.mp4 (24.4 MB)
    • 010 Prediction model evaluation with data split_ out-of-sample RMSE.mp4 (31.2 MB)
    • 025 018_LM_diamonds.R (2.2 KB)
    • 026 020_CorrelationLinear.R (0.8 KB)
    • 028 020_LM_Diagnosis.R (1.4 KB)
    • 029 021_AIC.R (0.5 KB)
    • 031 019_RMSE_LM.R (0.8 KB)
    • 032 022_RegressionModelValidation.R (0.9 KB)
    05 More types of regression models
    • 001 Lab_ Multiple linear regression - model estimation.mp4 (60.1 MB)
    • 002 Lab_ Multiple linear regression - prediction.mp4 (18.8 MB)
    • 003 Lab_ Multiple linear regression with interaction.mp4 (44.5 MB)
    • 004 Regression with Categorical Variables_ Dummy Coding Essentials in R.mp4 (29.7 MB)
    • 005 ANOVA - Categorical variables with more than two levels in linear regressions.mp4 (54.5 MB)
    • 033 029_MultipleLinearRegression.R (3.8 KB)
    • 035 030_MultipleLinearRegression_interactions.R (1.5 KB)
    • 036 031_DummyVariables.R (1.1 KB)
    • 037 032_ANOVA.R (1.2 KB)
    06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs
    • 001 Nonlinear Regression Essentials in R_ Polynomial and Spline Regression Models.mp4 (26.0 MB)
    • 002 Lab_ Polynomial regression in R.mp4 (64.9 MB)
    • 003 Lab_ Log transformation in R.mp4 (19.0 MB)
    • 004 Lab_ Spline regression in R.mp4 (47.0 MB)
    • 005 Lab_ Generalized additive models in R.mp4 (47.5 MB)
    • 039 033_PolynomialRegression.R (2.0 KB)
    • 040 034_PolyRegression_LogTransform.R (2.7 KB)
    • 041 035_SplineRegression.R (2.3 KB)
    • 042 036_GAM.R (0.4 KB)

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