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دسته بندی دوره ها

Machine Learning in R & Predictive Models | 3 Courses in 1

سرفصل های دوره

Supervised & unsupervised machine learning in R, clustering in R, predictive models in R by many labs, understand theory


01 - Introduction
  • 001 Introduction
  • 002 Motivation for the course Why to use Machine Learning for Predictions
  • 003 What is Machine Leraning and its main types
  • 004 Overview of Machine Leraning in R

  • 02 - Software used in this course R-Studio and Introduction to R
  • 001 Introduction to Section 2
  • 002 What is R and RStudio
  • 003 How to install R and RStudio in 2021
  • 004 Lab Install R and RStudio in 2021
  • 005 Introduction to RStudio Interface
  • 006 Lab Get started with R in RStudio

  • 03 - R Crash Course - get started with R-programming in R-Studio
  • 001 Introduction to Section 3
  • 002 Lab Installing Packages and Package Management in R
  • 003 Variables in R and assigning Variables in R
  • 004 Lab Variables in R and assigning Variables in R
  • 005 Overview of data types and data structures in R
  • 006 Lab data types and data structures in R
  • 007 Vectors operations in R
  • 008 Data types and data structures Factors
  • 009 Dataframes overview
  • 010 Functions in R - overview
  • 011 Lab For Loops in R
  • 012 Read Data into R
  • Files.zip

  • 04 - Fundamentals of predictive modelling with Machine Learning Thoery
  • 001 Overview of prediction process
  • 002 Components of the prediction models and trade-offs in prediction
  • 003 Lab your first prediction model in R
  • 004 Overfitting, sample errors in Machine Learning modelling in R
  • 005 Lab Overfitting, sample errors in Machine Learning modelling in R
  • 006 Study design for predictive modelling with Machine Learning
  • 007 Type of Errors and how to measure them
  • 008 Cross Validation in Machine Learning Models
  • 009 Data Selection for Machine Learning models
  • Files.zip

  • 05 - Unsupervised Machine Learning and Cluster Analysis in R
  • 001 Unsupervised Learning & Clustering theory
  • 002 Hierarchical Clustering Example
  • 003 Hierarchical Clustering Lab
  • 004 Hierarchical Clustering Merging points
  • 005 Heat Maps theory
  • 006 Heat Maps Lab
  • 007 Example K-Means Clustering in R Lab
  • 008 K-means clustering Application to email marketing
  • 009 Heatmaps to visualize K-Means Results in R Examplery Lab
  • 010 Selecting the number of clusters for unsupervised Clustering methods (K-Means)
  • 011 How to assess a Clustering Tendency of the dataset
  • 012 Assessing the performance of unsupervised learning (clustering) algorithms
  • Files.zip

  • 06 - Supervised Machine Learning in R Classification in R
  • 001 Overview of functionality of Caret R-package
  • 002 Supervised Machine Learning & KNN Overview
  • 003 Lab Supervised classification with K Nearest Neighbours algorithm in R
  • 005 Theory Confusion Matrix
  • 006 Lab Calculating Classification Accuray for logistic regression model
  • 007 Lab Receiver operating characteristic (ROC) curve and AUC
  • Files.zip

  • 07 - Supervised Machine Learning in R Linear Regression Analysis
  • 001 Overview of Regression Analysis
  • 002 Graphical Analysis of Regression Models
  • 003 Lab your first linear regression model
  • 004 Correlation in Regression Analysis in R Lab
  • 005 How to know if the model is best fit for your data - An overview
  • 006 Linear Regression Diagnostics
  • 007 AIC and BIC
  • 008 Evaluation of Prediction Model Performance in Supervised Learning Regression
  • 009 Lab Predict with linear regression model & RMSE as in-sample error
  • 010 Prediction model evaluation with data split out-of-sample RMSE
  • Files.zip

  • 08 - More types of regression models in R
  • 001 Lab Multiple linear regression - model estimation
  • 002 Lab Multiple linear regression - prediction
  • 003 Non-linear Regression Essentials in R Polynomial and Spline Regression Models
  • 004 Lab Polynomial regression in R
  • 005 Lab Log transformation in R
  • 006 Lab Spline regression in R
  • 007 Lab Generalized additive models in R
  • Files.zip

  • 09 - Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)
  • 001 Classification and Decision Trees (CART) Theory
  • 002 Lab Decision Trees in R
  • 003 Random Forest Theory
  • 004 Lab Random Forest in R
  • 006 Lab Machine Learning Models' Comparison & Best Model Selection
  • 008 Introduction to Model Selection Essentials in R
  • 009 Final Project Assignment
  • Files.zip

  • 10 - BONUS
  • 001 BONUS
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    تاریخ انتشار: 15 آبان 1403
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