R tidymodels part 1: Introduction

R, Data Science, tidymodels, tidyverse, Machine Learning, Modeling, Statistics, Regression, Predictive Modeling

What you'll learn
- What is data science, machine learning and how machine learning falls into AI domain
- How machine learning and statistics play an important role in predictive modeling
- What are the main machine learning tasks
- How to use core tidyverse libraries for day to day data science tasks
- How to wrangle your data with tidyverse libraries
- How tidymodels expand core tidyverse libraries
- The importance of exploratory data analysis when building predictive models
- What is a linear regression model
- How to develop a regression model using tidymodels
- Which steps are included in machine learning workflow
- How to design machine learning workflow using tidymodels
- What are general methods for feature selection
- What is penalized regression
- What is the essence of ridge, lasso and elastic net regression
- How to develop a different penalized regression models using tidymodels

Requirements
- R and RStudio already installed on your computer is a plus (we will show where to find all the sources online, and how to install it on your computer).
- Basic knowledge of statistics is a plus.
- Basic to intermediate R knowledge is a plus.
- If you are a complete beginner to programming or R, you will find this course quite challenging.
- Basic understanding of core tidyverse libraries is a plus (course also includes a tidyverse refresher crash course).
- Interest in data science, machine learning, statistics and building predictive models.
- Interest in how to write efficient R code.
- Please update R and / or R's libraries if necessary. List of versions ( R and all R's libraries used in the exercises) provided at the end of each section.

Description
Are you ready to move beyond data wrangling and start building realpredictive models in R?

This course is your next step!

Whether you're adata analyst,aspiring data scientist, or atidyverse userseeking to enhance your modeling skills, this course introduces you totidymodels, a powerful and consistentframework for statistical modeling and machine learning in R.

This course gives you a solid foundation inpredictive modeling. We begin with thefundamentals of regression modelingand guide you through thecomplete modeling workflowusingtidymodels:

Understandwhat modeling is— and how it's different from just analyzing data.

Grasp the principles ofstatistical learningandmachine learning.

Build,validate, andinterpret linear regression models.

Discover the power ofpenalized regression(ridge,lasso,elastic net).

Learn the concept of thebias-variance trade-offand how regularization helps.

Apply consistent,tidy workflowsfor:

preprocessingwithrecipes

modelingwithparsnip

resamplingwithrsample

evaluationwithyardstick

tuningwithtune

Start using best practices liketrain/test splits,cross-validation, andperformance metrics.

You'll walk away not just knowing how to use the tools, butunderstanding the modeling process itself!

Why Tidymodels?

Thetidymodels ecosystembrings the sameclarity,consistency, andelegancethat you love intidyverse, but formodeling.

Instead of jumping between inconsistent modeling functions and ad-hoc code, tidymodels lets you build, tune, and evaluate models using a well-structured andcoherent grammar.

What You’ll Get

Clearexplanationsof modeling concepts

Practical coding demosusingreal-world data

Step-by-stepmodeling workflows

DownloadableR scriptsanddatasets

Exercisesandassignmentsto reinforce learning

Solutionsfor all exercises and assignments

Lifetime access

If you’vemastered tidyverseand now want topredict,model, andexplain, then this course is your launchpad.

Enroll today and start building models the tidy way!!!

Who this course is for:
- Anyone who is interested in data science
- Anyone who is interested in statistics
- Anyone who is interested in building predictive models using machine learning
- Anyone who is interested in writing efficient R code
- Anyone whose job, research or hobby is related to building predictive models
- Aspiring data scientists, statisticians or machine learning engineers
- Anyone who deals with data modeling and would like to get familiar with modern R approach for modeling
- Students building predictive models
- Data scientist who mainly use python in their work, and would like to extend their skills into R domain
More Info