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

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

Basics to Deep Learning & Data Science using R. Indulge in data transformation/modeling with R basics, data science techniques, statistical machine learning models, deep learning, and Shiny app development. Elevate your skills with real-world challenges in this R Ultimate journey

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Get in-depth knowledge of a subject
Beginner level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Beginner level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Recall and describe R basics and advanced data science techniques

  • Explain methods to transform, visualize, and aggregate data with precision

  • Use ggplot, Plotly, and leaflet to craft compelling visuals

  • Develop dynamic web apps using R Shiny for engaging user experiences

Details to know

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Taught in English

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Specialization - 3 course series

What you'll learn

  • Recall the steps to install and configure R and RStudio

  • Explain how to manipulate various data types and structures in R

  • Use operators, loops, and functions to write efficient R code

  • Assess advanced data manipulation techniques such as piping, filtering, aggregation, reshaping, and joining datasets

Skills you'll gain

Category: Web Scraping
Category: Tidyverse (R Package)
Category: data manipulation
Category: Programming Principles
Category: Plotly
Category: Data Import/Export
Category: Development Environment
Category: Rmarkdown
Category: Data Cleansing
Category: Scripting
Category: ggplot2
Category: Data Structures
Category: Data Visualization
Category: Data Transformation
Category: Statistical Programming
Category: Scripting Languages
Category: Data Wrangling
Category: Data Visualization Software
Category: R programming

What you'll learn

  • Identify and describe core concepts of AI and machine learning

  • Explain and illustrate various regression analysis techniques to solve real-world problems

  • Utilize methods to build and evaluate robust machine learning models

  • Assess clustering and dimensionality reduction methods for data analysis

Skills you'll gain

Category: data manipulation
Category: Data Mining
Category: Supervised Learning
Category: Regression Analysis
Category: Classification And Regression Tree (CART)
Category: Machine Learning
Category: Artificial Intelligence
Category: Unsupervised Learning
Category: Reinforcement Learning
Category: Dimensionality Reduction
Category: Predictive Modeling
Category: Feature Engineering
Category: Random Forest Algorithm
Category: Applied Machine Learning
Category: Statistical Analysis
Category: Python programming

What you'll learn

  • Identify and recall deep learning foundations and applications

  • Explain how to develop and train neural network models

  • Use techniques to evaluate and optimize model performance

  • Assess the effectiveness of CNNs for image processing and semantic segmentation

Skills you'll gain

Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: PyTorch (Machine Learning Library)
Category: Time Series Analysis and Forecasting
Category: Shiny (R Package)
Category: Regression Analysis
Category: Data Processing
Category: Machine Learning Methods
Category: Classification And Regression Tree (CART)
Category: machine learning
Category: Image Analysis
Category: Unsupervised Learning
Category: Artificial Neural Networks
Category: Deep Learning
Category: Tensorflow
Category: Dimensionality Reduction
Category: Predictive Modeling
Category: Interactive Data Visualization
Category: Applied Machine Learning
Category: Statistical Machine Learning

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Instructor

Packt - Course Instructors
Packt
751 Courses119,808 learners

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