Data Science In R: Regression & Classification Analysis

Master Complete Hands-On Regression Analysis & Classification for applied Statistical Modelling & Machine Learning in R

What you'll learn
Your comprehensive guide to Regression Analysis & Classification for machine learning using R-programming language
It covers theory and applications of supervised machine learning with the focus on regression & classification analysis
Implement Machine Learning Techniques/Classification Such As Random Forests, SVM etc in R
Build machine learning based regression & classification models and test their robustness in R
Perform model's variable selection and assess regression model's accuracy
Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy
Compare different different machine learning models in R
Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
Graphically representing data in R before and after analysis
Requirements
Availability computer and internet & strong interest in the topic
Description
Regression Analysis and Classification for Machine Learning & Data Science in RMy course will be your hands-on guide to the theory and applications of supervised machine learning with a focus on regression analysis and classification 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 apply and understand REGRESSION ANALYSIS and CLASSIFICATION (Linear Regression, Random Forest, KNN, etc) in R. We will cover many R packages incl. caret package for supervised machine learning 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 & PRACTISEFully understand the basics of supervised Machine Learning for Regression Analysis and classification tasksHarness applications of parametric and non-parametric regressions & classification methods in RLearn how to apply correctly regression & classification models and test them in RLearn how to select the best machine learning model for your taskCarry out coding exercises & your independent project assignmentLearn 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 & 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 & Classification 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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What is Machine Leraning and it's main types?

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

Lecture 3 Introduction to Section 2

Lecture 4 What is R and RStudio?

Lecture 5 Lab: Install R and RStudio in 2020

Lecture 6 Lab: Get started with R in RStudio

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

Lecture 7 Introduction to Section

Lecture 8 Lab: Installing Packages and Package Management in R

Lecture 9 Lab: Variables in R and assigning Variables in R

Lecture 10 Overview of data types and data structures in R

Lecture 11 Lab: data types and data structures in R

Lecture 12 Vectors' operations in R

Lecture 13 Dataframes: overview in R

Lecture 14 Functions in R - overview

Lecture 15 Read Data into R

Section 4: Linear Regression in R

Lecture 16 Introduction to Regression Analysis

Lecture 17 Graphical Analysis of Regression Models

Lecture 18 Lab: your first linear regression model

Lecture 19 Correlation in Regression Analysis in R: Lab

Lecture 20 How to know if the model is best fit for your data - An overview

Lecture 21 Linear Regression Diagnostics

Lecture 22 AIC and BIC

Lecture 23 Evaluation of Performance of Regression-based Prediction Model

Lecture 24 Lab: Predict with linear regression model & RMSE as in-sample error

Lecture 25 Prediction model evaluation with data split: out-of-sample RMSE

Section 5: More types of regression models in R

Lecture 26 Lab: Multiple linear regression - model estimation

Lecture 27 Lab: Multiple linear regression - prediction

Lecture 28 Nonlinear Regression Essentials in R: Polynomial and Spline Regression Models

Lecture 29 Lab: Polynomial regression in R

Lecture 30 Lab: Log transformation in R

Lecture 31 Lab: Spline regression in R

Lecture 32 Lab: Generalized additive models in R

Lecture 33 Introduction to Model Selection Essentials in R

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

Lecture 34 Supervised Machine Learning & KNN: Overview

Lecture 35 Overview of functionality of Caret R-package

Lecture 36 Lab: Supervised classification with K Nearest Neighbours algorithm in R

Lecture 37 Theory: Confusion Matrix

Lecture 38 Lab: Calculating Classification Accuracy for logistic regression model

Lecture 39 Lab: Receiver operating characteristic (ROC) curve and AUC

Section 7: Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)

Lecture 40 Classification and Decision Trees (CART): Theory

Lecture 41 Lab: Decision Trees in R

Lecture 42 Random Forest: Theory

Lecture 43 Lab: Random Forest in R

Lecture 44 Lab: Machine Learning Models' Comparison & Best Model Selection

Lecture 45 Final Project Assignment

Lecture 46 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