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

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

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

Language: english

Note: 4.6/5 (75 notes) 15,567 students

Instructor(s): Kate Alison

Last update: 2021-11-04

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

 

Requirements

  • Availability computer and internet & strong interest in the topic

 

Description

Machine Learning in R & Predictive Models |Theory & Practice

My 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 level

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

  • Fully understand the basics of Machine Learning, Cluster Analysis & Prediction Models from theory to practice

  • Harness applications of supervised machine learning (classification and regressions) and Unsupervised machine learning (cluster analysis) in R

  • Learn how to apply correctly prediction models and test them in R

  • Complete programming & data science tasks in an independent project on Supervised Machine Learning in R

  • Implement Unsupervised Clustering Techniques (k-means Clustering and Hierarchical Clustering etc)

  • 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 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!


 

Who this course is for

  • 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 content

  • Introduction
    • Introduction
    • Motivation for the course: Why to use Machine Learning for Predictions?
    • What is Machine Leraning and it’s main types?
    • Overview of Machine Leraning in R
    • Machine Learning Types
  • Software used in this course R-Studio and Introduction to R
    • Introduction to Section 2
    • What is R and RStudio?
    • How to install R and RStudio in 2021
    • Lab: Install R and RStudio in 2021
    • Introduction to RStudio Interface
    • Lab: Get started with R in RStudio
    • What is the current version of R and R-Studio
  • R Crash Course – get started with R-programming in R-Studio
    • Introduction to Section 3
    • Lab: Installing Packages and Package Management in R
    • Variables in R and assigning Variables in R
    • Lab: Variables in R and assigning Variables in R
    • Overview of data types and data structures in R
    • Lab: data types and data structures in R
    • Vectors’ operations in R
    • Data types and data structures: Factors
    • Dataframes: overview
    • Functions in R – overview
    • Lab: For Loops in R
    • Read Data into R
  • Fundamentals of predictive modelling with Machine Learning: Thoery
    • Overview of prediction process
    • Components of the prediction models and trade-offs in prediction
    • Lab: your first prediction model in R
    • Overfitting, sample errors in Machine Learning modelling in R
    • Lab: Overfitting, sample errors in Machine Learning modelling in R
    • Study design for predictive modelling with Machine Learning
    • Type of Errors and how to measure them
    • Cross Validation in Machine Learning Models
    • Data Selection for Machine Learning models
  • Unsupervised Machine Learning and Cluster Analysis in R
    • Unsupervised Learning & Clustering: theory
    • Hierarchical Clustering: Example
    • Hierarchical Clustering: Lab
    • Hierarchical Clustering: Merging points
    • Heat Maps: theory
    • Heat Maps: Lab
    • Example K-Means Clustering in R: Lab
    • K-means clustering: Application to email marketing
    • Heatmaps to visualize K-Means Results in R: Examplery Lab
    • Selecting the number of clusters for unsupervised Clustering methods (K-Means)
    • How to assess a Clustering Tendency of the dataset
    • Assessing the performance of unsupervised learning (clustering) algorithms
  • Supervised Machine Learning in R: Classification in R
    • Overview of functionality of Caret R-package
    • Supervised Machine Learning & KNN: Overview
    • Lab: Supervised classification with K Nearest Neighbours algorithm in R
    • Classification with the KNN-algorithm
    • Theory: Confusion Matrix
    • Lab: Calculating Classification Accuray for logistic regression model
    • Lab: Receiver operating characteristic (ROC) curve and AUC
  • Supervised Machine Learning in R: Linear Regression Analysis
    • Overview of Regression Analysis
    • Graphical Analysis of Regression Models
    • Lab: your first linear regression model
    • Correlation in Regression Analysis in R: Lab
    • How to know if the model is best fit for your data – An overview
    • Linear Regression Diagnostics
    • AIC and BIC
    • Evaluation of Prediction Model Performance in Supervised Learning: Regression
    • Lab: Predict with linear regression model & RMSE as in-sample error
    • Prediction model evaluation with data split: out-of-sample RMSE
  • More types of regression models in R
    • Lab: Multiple linear regression – model estimation
    • Lab: Multiple linear regression – prediction
    • Non-linear Regression Essentials in R: Polynomial and Spline Regression Models
    • Lab: Polynomial regression in R
    • Lab: Log transformation in R
    • Lab: Spline regression in R
    • Lab: Generalized additive models in R
  • Model (and Predictors) Selection Essentials in R
    • Introduction to Model Selection Essentials in R
  • Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)
    • Classification and Decision Trees (CART): Theory
    • Lab: Decision Trees in R
    • Random Forest: Theory
    • Lab: Random Forest in R
    • Parametrise Random Forest model
    • Lab: Machine Learning Models’ Comparison & Best Model Selection
    • Predict using the best model
    • Final Project Assignment
  • BONUS
    • BONUS

 

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

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