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R Ultimate 2023 - R for Data Science and Machine Learning
R Ultimate 2023 - R for Data Science and Machine Learning

R Ultimate 2023 - R for Data Science and Machine Learning: Master R from Basics to Deep Learning, Unveiling Data Science, Statistical Models, Shiny, and Beyond

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Profile Icon Bert Gollnick
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€59.99
Video Sep 2023 22hrs 16mins 1st Edition
Video
€59.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Bert Gollnick
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€59.99
Video Sep 2023 22hrs 16mins 1st Edition
Video
€59.99
Subscription
Free Trial
Renews at €18.99p/m
Video
€59.99
Subscription
Free Trial
Renews at €18.99p/m

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Key benefits

  • Learn R fundamentals, advanced analytics, machine learning, and deep learning for data science
  • Work on practical labs and exercises to reinforce data manipulation, modeling, and visualization
  • Equip for practical data, projects, case studies, and translate theory into actionable insights

Description

R is a programming language and environment designed for statistical computing, data analysis, and graphical representation. R is widely used by statisticians, data scientists, researchers, and analysts for various tasks related to data manipulation, statistical modeling, and visualization. R is particularly well-suited for tasks involving data analysis, visualization, and statistics, chosen for its flexibility and a wide array of available tools. This course takes us on a transformative journey through R programming, from foundational concepts to cutting-edge techniques. We delve into R’s fundamentals, data types, variables, and structures. We will explore R programming with custom functions, control structures, and data manipulation. We will analyze data visualization with leading packages, statistical analysis, hypothesis testing, and regression modeling. With regular expressions, we will understand advanced data manipulation, outlier handling, missing data strategies, and text manipulation. We will learn about ML with regression, classification, and clustering algorithms. We will explore DL, neural networks, image classification, and semantic segmentation. Upon completion, we will create dynamic web apps with Shiny and emerge as skilled R practitioners, ready to tackle challenges and contribute to data-driven decision-making.

Who is this book for?

The course caters to aspiring and established data scientists, analysts, programmers, researchers, and professionals seeking to enhance their skills in data manipulation, statistical analysis, ML, and DL using R programming. It caters to individuals with varying experience levels, from beginners looking to enter the field to experienced practitioners aiming to expand their expertise in data-driven decision-making and advanced analytics. Prerequisites include prior programming experience but this course can accommodate learners with varying levels of data science concepts and R programming familiarity.

What you will learn

  • Excel in R basics and advanced data science techniques
  • Transform, visualize, and aggregate data with precision
  • Craft compelling visuals using ggplot, Plotly, and leaflet
  • Implement regression, classification, and clustering models
  • Explore neural networks, image classification, and segmentation
  • Develop dynamic web apps using R Shiny for engaging user experiences

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 22, 2023
Length: 22hrs 16mins
Edition : 1st
Language : English
ISBN-13 : 9781835082539
Category :
Languages :
Concepts :
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Product Details

Publication date : Sep 22, 2023
Length: 22hrs 16mins
Edition : 1st
Language : English
ISBN-13 : 9781835082539
Category :
Languages :
Concepts :
Tools :

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Table of Contents

29 Chapters
Course Introduction Chevron down icon Chevron up icon
Data Types and Structures Chevron down icon Chevron up icon
R Programming Chevron down icon Chevron up icon
Data Import and Export Chevron down icon Chevron up icon
Basic Data Manipulation Chevron down icon Chevron up icon
Data Visualization Chevron down icon Chevron up icon
Advanced Data Manipulation Chevron down icon Chevron up icon
Machine Learning: Introduction Chevron down icon Chevron up icon
Machine Learning: Regression Chevron down icon Chevron up icon
Machine Learning: Model Preparation and Evaluation Chevron down icon Chevron up icon
Machine Learning: Regularization Chevron down icon Chevron up icon
Machine Learning: Classification Basics Chevron down icon Chevron up icon
Machine Learning: Classification with Decision Trees Chevron down icon Chevron up icon
Machine Learning: Classification with Random Forests Chevron down icon Chevron up icon
Machine Learning: Classification with Logistic Regression Chevron down icon Chevron up icon
Machine Learning: Classification with Support Vector Machines Chevron down icon Chevron up icon
Machine Learning: Classification with Ensemble Models Chevron down icon Chevron up icon
Machine Learning: Association Rules Chevron down icon Chevron up icon
Machine Learning: Clustering Chevron down icon Chevron up icon
Machine Learning: Dimensionality Reduction Chevron down icon Chevron up icon
Machine Learning: Reinforcement Learning Chevron down icon Chevron up icon
Deep Learning: Introduction Chevron down icon Chevron up icon
Deep Learning: Regression Chevron down icon Chevron up icon
Deep Learning: Classification Chevron down icon Chevron up icon
Deep Learning: Convolutional Neural Networks Chevron down icon Chevron up icon
Deep Learning: Autoencoders Chevron down icon Chevron up icon
Deep Learning: Transfer Learning and Pretrained Networks Chevron down icon Chevron up icon
Deep Learning: Recurrent Neural Networks Chevron down icon Chevron up icon
Shiny Chevron down icon Chevron up icon
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