4.42 (18 reviews)
☑ 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
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 modeling 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 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 & PRACTISE
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
NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:
You’ll start by absorbing the most valuable R 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 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 Machine Learning and modeling 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 the 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!
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
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 2020
Lab: Install R and RStudio in 2020
Introduction to RStudio Interface
Lab: Get started with R in RStudio
Overview of prediction process in Machine Learning
Lab: your first prediction model in R
Overview of prediction process
Components of the prediction models and trade-offs in prediction
R Crash Course - get started with R-programming in R-Studio
Introduction to Section 4
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
Data types and data structures: Factors
Functions in R - overview
Lab: For Loops in R
Read Data into R
Fundamentals of predictive modelling with Machine Learning: Thoery
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
K-Means Clustering: Theory
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
Supervised Machine Learning & KNN: Overview
Lab: Supervised classification with K Nearest Neighbours algorithm in R
Classification with the KNN-algorithm
Overview of functionality of Caret R-package
Supervised Learning: Classification Performance Evaluation in R
Theory: Confusion Matrix
Lab: Calculating Classification Accuray 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
Supervised Machine Learning in R: Linear Regression Analysis
Regression: Short Overview
Graphical Analysis for Regression in R and 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
Supervised Learning: Regression Model Performance Evaluation in R
Evaluation of Prediction Model Performance in Supervised Learning: Regression
Predict with linear regression model & RMSE as in-sample error
Prediction model evaluation with data split: out-of-sample RMSE
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
Parametrise Random Forest model
Machine Learning Models' Comparison & Final Project
Lab: Machine Learning Models' Comparison & Best Model Selection
Predict using the best model
Final Project Assignment
Really awesome and exactly what I was looking for!!! That is a unique course on UDEMY offering to learn Machine Learning application in geospatial analysis. I am super grateful to Kate for such amazing course.
It is a wonderful and quite comprehensive course in Machine Learning in R. I loved a prt on predictive models – I think, nobody teaches it except Kate on Udemy. I like the way tutor teaches. Content is brilliant. It's awesome.
I really enjoy the sequence of the theory from the easier predictive models to more complicated. It was explained in very understandable way. And supported by R-codes that are well commented and good to follow. I am ready to start to play with data.
This was a great course. I learned a lot in a short period of time. Very knowledgeable instructors - I hope they do more courses in R.
This course is a MUST have for anyone interested in machine learning in R. Instructors do an amazing job of turning extremely complex topics into easily understandable chunks. Recommend highly!
This is an excellent program with excellent explanations and a knowledge-sharing course. Really good course over some key python libs for Data Science and machine learning with intuitive and complete code examples and small projects. Good discussions and intuitions on solutions and approaching the problem.
Wow, that is really a great course on Machine Learning and Prediction models. I loved a mixture of theory lectures, practicl labs in R and coding exercises! Great learning curve and now I am excited to start applying the new skill to my work!
The course will give take you from a complete beginner to an advanced level in data science & machine learning. Happy with the content.