Machine Learning in R Predictive Models 3 Courses in 1

Supervised & unsupervised machine learning in R, clustering in R, predictive models in R by many labs, understand theory
Machine Learning in R Predictive Models 3 Courses in 1
File Size :
3.69 GB
Total length :
7h 36m



Kate Alison


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Machine Learning in R Predictive Models 3 Courses in 1

What you’ll learn

Your complete guide to unsupervised & supervised machine learning and predictive modeling using R-programming language
It covers both theoretical background of MACHINE LERANING & and predictive modeling as well as practical examples in R and R-Studio
Fully understand the basics of Machine Learning, Cluster Analysis & Predictive Modelling
Highly practical data science examples related to supervised machine learning, clustering & prediction modelling in R
Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
Be Able To Harness The Power of R For Practical Data Science
Compare different different machine learning algorithms for regression & classification modelling
Apply statistical and machine learning based regression & classification models to real data
Build machine learning based regression & classification models and test their robustness in R
Learn when and how machine learning & predictive models should be correctly applied
Test your skills with multiple coding exercices and final project that you will ommplement independently
Implement Machine Learning Techniques/Classification Such As Random Forests, SVM etc in R
You’ll have a copy of the scripts used in the course for your reference to use in your analysis

Machine Learning in R Predictive Models 3 Courses in 1


Availability computer and internet & strong interest in the topic


Machine Learning in R & Predictive Models |Theory & PracticeMy course will be your complete guide to the theory and applications of supervised & unsupervised machine learning and predictive modelling using the R-programming language. This course also combines the material of 3 independent courses related to (1) R-programming, (2) Machine Learning and (3) Predictive modelling.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 MACHINE LEARNING & PREDICTIVE MODELS (K-means, Random Forest, SVM, logistic regression, etc) in R (many R packages incl. caret package will be covered). This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (classification & regressions) and unsupervised clustering techniques. 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.In this age of big data, companies across the globe use R to analyze big volumes of data for business and research. By becoming proficient in supervised & unsupervised machine learning and predictive modeling in R, you can give your company a competitive edge and boost your career to the next levelTHIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTICEFully understand the basics of Machine Learning, Cluster Analysis & Prediction Models from theory to practiceHarness applications of supervised machine learning (classification and regressions) and Unsupervised machine learning (cluster analysis) in RLearn how to apply correctly prediction models and test them in RComplete programming & data science tasks in an independent project on Supervised Machine Learning in RImplement Unsupervised Clustering Techniques (k-means Clustering and Hierarchical Clustering etc)Learn the basics of R-programmingGet a copy of all scripts used in the courseand MORENO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:You’ll start by absorbing the most valuable Machine Learning, Predictive Modelling & Data Science 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 Machine Learning course in R, 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 is a full introduction to R & R programming in this course.This course is different from other training resources. Each lecture seeks to enhance your Machine Learning and modelling 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!


Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Motivation for the course: Why to use Machine Learning for Predictions?

Lecture 3 What is Machine Leraning and it’s main types?

Lecture 4 Overview of Machine Leraning in R

Section 2: Software used in this course R-Studio and Introduction to R

Lecture 5 Introduction to Section 2

Lecture 6 What is R and RStudio?

Lecture 7 How to install R and RStudio in 2021

Lecture 8 Lab: Install R and RStudio in 2021

Lecture 9 Introduction to RStudio Interface

Lecture 10 Lab: Get started with R in RStudio

Section 3: R Crash Course – get started with R-programming in R-Studio

Lecture 11 Introduction to Section 3

Lecture 12 Lab: Installing Packages and Package Management in R

Lecture 13 Variables in R and assigning Variables in R

Lecture 14 Lab: Variables in R and assigning Variables in R

Lecture 15 Overview of data types and data structures in R

Lecture 16 Lab: data types and data structures in R

Lecture 17 Vectors’ operations in R

Lecture 18 Data types and data structures: Factors

Lecture 19 Dataframes: overview

Lecture 20 Functions in R – overview

Lecture 21 Lab: For Loops in R

Lecture 22 Read Data into R

Section 4: Fundamentals of predictive modelling with Machine Learning: Thoery

Lecture 23 Overview of prediction process

Lecture 24 Components of the prediction models and trade-offs in prediction

Lecture 25 Lab: your first prediction model in R

Lecture 26 Overfitting, sample errors in Machine Learning modelling in R

Lecture 27 Lab: Overfitting, sample errors in Machine Learning modelling in R

Lecture 28 Study design for predictive modelling with Machine Learning

Lecture 29 Type of Errors and how to measure them

Lecture 30 Cross Validation in Machine Learning Models

Lecture 31 Data Selection for Machine Learning models

Section 5: Unsupervised Machine Learning and Cluster Analysis in R

Lecture 32 Unsupervised Learning & Clustering: theory

Lecture 33 Hierarchical Clustering: Example

Lecture 34 Hierarchical Clustering: Lab

Lecture 35 Hierarchical Clustering: Merging points

Lecture 36 Heat Maps: theory

Lecture 37 Heat Maps: Lab

Lecture 38 Example K-Means Clustering in R: Lab

Lecture 39 K-means clustering: Application to email marketing

Lecture 40 Heatmaps to visualize K-Means Results in R: Examplery Lab

Lecture 41 Selecting the number of clusters for unsupervised Clustering methods (K-Means)

Lecture 42 How to assess a Clustering Tendency of the dataset

Lecture 43 Assessing the performance of unsupervised learning (clustering) algorithms

Section 6: Supervised Machine Learning in R: Classification in R

Lecture 44 Overview of functionality of Caret R-package

Lecture 45 Supervised Machine Learning & KNN: Overview

Lecture 46 Lab: Supervised classification with K Nearest Neighbours algorithm in R

Lecture 47 Theory: Confusion Matrix

Lecture 48 Lab: Calculating Classification Accuray for logistic regression model

Lecture 49 Lab: Receiver operating characteristic (ROC) curve and AUC

Section 7: Supervised Machine Learning in R: Linear Regression Analysis

Lecture 50 Overview of Regression Analysis

Lecture 51 Graphical Analysis of Regression Models

Lecture 52 Lab: your first linear regression model

Lecture 53 Correlation in Regression Analysis in R: Lab

Lecture 54 How to know if the model is best fit for your data – An overview

Lecture 55 Linear Regression Diagnostics

Lecture 56 AIC and BIC

Lecture 57 Evaluation of Prediction Model Performance in Supervised Learning: Regression

Lecture 58 Lab: Predict with linear regression model & RMSE as in-sample error

Lecture 59 Prediction model evaluation with data split: out-of-sample RMSE

Section 8: More types of regression models in R

Lecture 60 Lab: Multiple linear regression – model estimation

Lecture 61 Lab: Multiple linear regression – prediction

Lecture 62 Non-linear Regression Essentials in R: Polynomial and Spline Regression Models

Lecture 63 Lab: Polynomial regression in R

Lecture 64 Lab: Log transformation in R

Lecture 65 Lab: Spline regression in R

Lecture 66 Lab: Generalized additive models in R

Section 9: Model (and Predictors) Selection Essentials in R

Lecture 67 Introduction to Model Selection Essentials in R

Section 10: Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)

Lecture 68 Classification and Decision Trees (CART): Theory

Lecture 69 Lab: Decision Trees in R

Lecture 70 Random Forest: Theory

Lecture 71 Lab: Random Forest in R

Lecture 72 Lab: Machine Learning Models’ Comparison & Best Model Selection

Lecture 73 Final Project Assignment

Section 11: BONUS

Lecture 74 BONUS

The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field.,Everyone who would like to learn Data Science Applications in the R & R Studio Environment,Everyone who would like to learn theory and implementation of Machine Learning On Real-World Data

Course Information:

Udemy | English | 7h 36m | 3.69 GB
Created by: Kate Alison

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