20-days-ML-in-R

This repository contains notes, exercises, and code created while completing the Introduction to Machine Learning with R learning track from Simplilearn SkillUp . The work is organized as a focused 20‑day journey to build strong fundamentals in machine learning using R.

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Repository structure

• ML in R Certification Course/ – Main working directory with daily folders, R scripts, and notebooks that follow the course lessons and practice activities.
• .gitignore – Rules to exclude temporary, cache, and large output files (such as .Rhistory, .Rproj.user, and generated data) from version control.
• README.md – Overview of the project, setup instructions, and learning roadmap.

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Course overview

The Introduction to Machine Learning with R course is a free, beginner‑friendly program of around 10 hours of self‑paced content. It introduces R programming from scratch and then moves into essential machine learning algorithms and their applications.

Main themes of the course:

• Machine learning basics, terminology, and real‑world applications.
• R programming foundations: setup, variables, data types, operators, and basic syntax.
• Working with core R data structures: vectors, lists, matrices, and data frames.
• Data manipulation with dplyr and tidyr, and data visualization in R.
• Supervised learning in R: linear regression, logistic regression, decision trees, random forests, and support vector machines.
• Unsupervised learning: hierarchical clustering in R.
• Time series analysis in R and its basic use cases.

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Learning goals

This repository is meant to show consistent progress and practical understanding of the course content.

By the end of the 20 days, the goals are to:

• Understand fundamental machine learning concepts and when to apply different types of algorithms.
• Write clean R code for loading, cleaning, transforming, and visualizing data.
• Build, tune, and evaluate basic regression and classification models in R using real or sample datasets.
• Apply clustering and simple time series models to explore patterns in data.
• Maintain a clear, well‑structured project that can be shared as a portfolio artifact.

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20‑day study plan

An example structure for the 20‑day journey (adapt to your actual progress):

• Days 1–3: R setup, syntax, variables, data types, vectors, lists, matrices, data frames, and control flow.
• Days 4–5: Functions in R, data manipulation with dplyr and tidyr, and basic visualization.
• Days 6–8: Machine learning fundamentals, problem framing, and exploratory data analysis.
• Days 9–12: Linear regression and logistic regression in R, including model evaluation metrics.
• Days 13–15: Decision trees, random forests, and SVM in R, plus handling overfitting and basic tuning.
• Days 16–17: Hierarchical clustering and interpretation of clusters.
• Days 18–19: Time series analysis in R: decomposition and basic forecasting ideas.
• Day 20: Mini‑project that combines data cleaning, modeling, and visualization into a single end‑to‑end workflow.

Create a markdown note for each day (for example Day01.md) to log what was learned, link to scripts, and record reflections or next steps.