R Ultimate 2024 - R for Data Science and Machine Learning

Packt via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Beginner

Found in

Overview

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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive course takes you from R programming basics to advanced machine learning and deep learning. You’ll gain hands-on skills in data manipulation, visualization, and statistical modeling using R. The journey begins with RStudio setup and foundational programming, then moves into real-world data projects, including web scraping, data cleaning, and regression models. You'll explore visualization tools like ggplot2 and Plotly, and delve into supervised and unsupervised learning. Advanced modules cover neural networks, CNNs, autoencoders, and Shiny app deployment. You'll also apply PCA, t-SNE, clustering, and reinforcement learning for complex data tasks. This course suits aspiring data scientists, analysts, and ML enthusiasts. Prior coding experience is helpful but not required. It's designed for beginner to intermediate learners.

Syllabus

Course 1: Foundations of R Programming and Basic Data Manipulation
Course 2: Intermediate Data Manipulation and Machine Learning
Course 3: Advanced Machine Learning and Deep Learning

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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This advanced machine learning and deep learning course provides a robust foundation in these transformative technologies. Starting with an overview of deep learning, you'll explore its core concepts, real-world applications, and significance in AI's evolution. Practical aspects include neural network layers, activation functions, and performance metrics in model evaluation. Through hands-on coding labs, you'll cover regression, classification, and convolutional neural networks (CNNs), building and fine-tuning models, understanding loss functions, and using optimizers for accuracy. Emphasis is on frameworks like TensorFlow and PyTorch for developing robust neural networks. The course concludes with specialized topics such as autoencoders, transfer learning, and recurrent neural networks (RNNs). Interactive labs and projects will apply knowledge to complex data analysis, time-series prediction, and creating web applications with Shiny. Ideal for data scientists, machine learning engineers, and AI enthusiasts, prerequisites include Python proficiency and basic machine learning knowledge.
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Embark on a transformative journey into R programming and data manipulation with this comprehensive course. It starts with an in-depth overview of R and RStudio, covering installation, configuration, and key features. You'll master navigating RStudio, managing projects, and handling diverse file formats for efficient workflows. The course delves into Rmarkdown for dynamic documentation, blending code, narrative, and visualizations. You'll explore essential data types and structures through hands-on labs, including matrices, arrays, lists, data frames, strings, and DateTime objects. The R programming section covers operators, loops, and functions, enabling you to write clean, modular code. Advanced topics include data import/export, web scraping, and sophisticated data manipulation techniques using piping, filtering, aggregation, reshaping, and joining datasets. You'll create impactful visualizations with ggplot2, plotly, leaflet, and dygraphs. Ideal for aspiring data scientists, analysts, and professionals, this course requires a basic programming understanding and targets beginners to intermediate learners, ensuring you transform raw data into actionable insights and compelling visualizations.
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you will explore artificial intelligence (AI) and its core concepts, forming a solid foundation for machine learning. You will delve into regression analysis, applying univariate, polynomial, and multivariate regression techniques to real-world problems through interactive labs. Next, you will learn model preparation and evaluation, focusing on underfitting, overfitting, data splitting, and resampling methods, alongside regularization techniques to enhance model performance. The course covers classification methods, including confusion matrices, ROC curves, decision trees, random forests, logistic regression, and support vector machines, all paired with practical labs. You will also explore ensemble models and association rules, like the Apriori algorithm, to uncover hidden data patterns. Designed for data scientists, machine learning enthusiasts, and technical professionals, this course requires a basic understanding of machine learning concepts and Python programming. Learning outcomes include grasping AI and machine learning fundamentals, applying regression analysis, building and evaluating models, implementing classification techniques, performing clustering and dimensionality reduction, uncovering patterns with association rules, and applying reinforcement learning principles.