Data Science in R: Regression & Classification Analysis
- Description
- Curriculum
- FAQ
- Reviews
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
- and MORE
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!
-
9Introduction to SectionVideo lesson
-
10Lab: Installing Packages and Package Management in RVideo lesson
-
11Lab: Variables in R and assigning Variables in RVideo lesson
-
12Overview of data types and data structures in RVideo lesson
-
13Lab: data types and data structures in RVideo lesson
-
14Vectors' operations in RVideo lesson
-
15Dataframes: overview in RVideo lesson
-
16Functions in R - overviewVideo lesson
-
17Read Data into RVideo lesson
-
18Introduction to Regression AnalysisVideo lesson
-
19Introduction to Regression AnalysisQuiz
Introduction to Regression Analysis
-
20Graphical Analysis of Regression ModelsVideo lesson
-
21Lab: your first linear regression modelVideo lesson
-
22Correlation in Regression Analysis in R: LabVideo lesson
-
23How to know if the model is best fit for your data - An overviewVideo lesson
-
24Linear Regression DiagnosticsVideo lesson
-
25AIC and BICVideo lesson
-
26Evaluation of Performance of Regression-based Prediction ModelVideo lesson
-
27Lab: Predict with linear regression model & RMSE as in-sample errorVideo lesson
-
28Prediction model evaluation with data split: out-of-sample RMSEVideo lesson
-
29Lab: Multiple linear regression - model estimationVideo lesson
-
30Lab: Multiple linear regression - predictionVideo lesson
-
31Nonlinear Regression Essentials in R: Polynomial and Spline Regression ModelsVideo lesson
-
32Lab: Polynomial regression in RVideo lesson
-
33Lab: Log transformation in RVideo lesson
-
34Lab: Spline regression in RVideo lesson
-
35Lab: Generalized additive models in RVideo lesson
-
36Introduction to Model Selection Essentials in RVideo lesson
-
37Supervised Machine Learning & KNN: OverviewVideo lesson
-
38Overview of functionality of Caret R-packageVideo lesson
-
39Lab: Supervised classification with K Nearest Neighbours algorithm in RVideo lesson
-
40Classification with the KNN-algorithmQuiz
-
41Theory: Confusion MatrixVideo lesson
-
42Lab: Calculating Classification Accuracy for logistic regression modelVideo lesson
-
43Compare the model accuracy (or any other metric) using thresholds of 0.1 and 0.9.Quiz
-
44Lab: Receiver operating characteristic (ROC) curve and AUCVideo lesson
External Links May Contain Affiliate Links read more