5.00 (4 reviews)
☑ 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
Regression Analysis and Classification 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 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 & PRACTISE
Fully understand the basics of supervised Machine Learning for Regression Analysis and classification tasks
Harness applications of parametric and non-parametric regressions & classification methods in R
Learn how to apply correctly regression & classification models and test them in R
Learn how to select the best 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
NO 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!
What is Machine Leraning and it's main types?
Machine Learning Types
Software used in this course R-Studio and Introduction to R
Introduction to Section 2
What is R and RStudio?
Lab: Install R and RStudio in 2020
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
Lab: Installing Packages and Package Management 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
Dataframes: overview in R
Functions in R - overview
Read Data into R
Linear Regression in R
Introduction to Regression Analysis
Introduction to 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 Performance of Regression-based Prediction Model
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
Nonlinear 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
Introduction to Model Selection Essentials in R
Supervised Machine Learning in R: Classification in R
Supervised Machine Learning & KNN: Overview
Overview of functionality of Caret R-package
Lab: Supervised classification with K Nearest Neighbours algorithm in R
Classification with the KNN-algorithm
Theory: Confusion Matrix
Lab: Calculating Classification Accuracy for logistic regression model
Compare the model accuracy (or any other metric) using thresholds of 0.1 and 0.9.
Lab: Receiver operating characteristic (ROC) curve and AUC
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
I really enjoyed the way you explained, and it's very easy for me to understand the concepts. The R course and practical labs are very helpful! Thank you so much!
Explanations and labs are very useful and are in simple language. Good content and material are provided in the course. overall a satisfactory experience achieved.
Machine learning was a totally new concept for me. I assumed that it would be very difficult to understand. This course helped me to be confident in mastering the concepts of Regression and Classification in R.