Skillshare Data Science and Machine Learning with R Masterclass

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size12.3 GB
  • Uploaded ByPBDR
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  • Last checkedApr. 08th '21
  • Date uploadedApr. 05th '21
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About This Class
Learn Data Science and Machine Learning with R Masterclass

In this practical, hands-on class you're going to learn how to use R programming language for Data Science and Machine Learning!

Even if you already have some experience, or want to learn about the advanced features of Machine Learning and Data Science with R, this course is for you!

In this class you’ll learn:

How to create web apps with R Shiny
How to create markdown reports
R basics and fundatmentals
R intermediate and advanced functions
Data cleaning, processing, wrangling, visualization, and manipulation
Machine Learning and its various practical applications
How to use the various scripting and libraries within R
Machine Learning concepts and algorithms
Supervised vs unsupervised Machine Learning
Regression, classification, and clustering
How to build custom data solutions
How to create a professional data scientist resume
No matter what the scenario or how complicated a data problem may be, this class gives you the foundational training you need to solve real-world problems using Data Science and Machine Learning with R – and start pursuing a career in a field that is increasingly in demand as the global reliance on technology grows.


Title: Data Science and Machine Learning with R Masterclass
Publisher: Skillshare
Category: Technology
Size: 12587M
Files: 21F
Date: 2021-04-03
Course #: 1829727856
Published: Skillshare
Updated: N/A
URL: https://www.skillshare.com/classes/Data-Science-and-Machine-Learning-with-R-Masterclass/1829727856?category=technology
Author: Juan Galvan
Duration: 1d 4h 14m

Files:

Skillshare Data Science and Machine Learning with R Masterclass
  • 68-hands-on_exploratory_data_analysis.mkv (606.9 MB)
  • 66-linear_regression_a_simple_model.mkv (558.3 MB)
  • 64-data_preprocessing.mkv (459.2 MB)
  • 62-intro_to_machine_learning___part_2.mkv (446.9 MB)
  • 27-intermediate_r_section_intro.mkv (434.6 MB)
  • 49-web_scraping.mkv (389.7 MB)
  • 47-data_pivoting.mkv (365.1 MB)
  • 26-data_frames-tibbles.mkv (363.1 MB)
  • 53-single_variable_plots.mkv (341.9 MB)
  • 39-data_manipulation_in_r_section_intro.mkv (334.3 MB)
  • 44-the_mutate_verb.mkv (301.2 MB)
  • 70-linear_regression_a_real_model.mkv (269.6 MB)
  • 38-databases.mkv (257.7 MB)
  • 65-linear_regression_a_simple_model_intro.mkv (246.3 MB)
  • 25-data_frames_helper_functions.mkv (246.3 MB)
  • 13-data_types_and_structures_section_intro.mkv (245.7 MB)
  • 57-intro_to_r_markdown.mkv (239.4 MB)
  • 58-intro_to_r_shiny.mkv (232.5 MB)
  • 52-aesthetics_mappings.mkv (220.1 MB)
  • 67-exploratory_data_analysis_intro.mkv (219.6 MB)
  • 63-data_preprocessing_intro.mkv (219.4 MB)
  • 35-dates_and_times.mkv (217.3 MB)
  • 43-the_select_verb.mkv (213.6 MB)
  • 34-factors.mkv (199.7 MB)
  • 16-vectors_part_two.mkv (188.7 MB)
  • 42-the_filter_verb.mkv (188.4 MB)
  • 72-logistic_regression_in_r.mkv (186.4 MB)
  • 60-other_webapp_examples.mkv (179.7 MB)
  • 55-facets_layering_and_coordinate_systems.mkv (179.0 MB)
  • 22-working_with_lists.mkv (177.0 MB)
  • 46-the_summarize_verb.mkv (176.8 MB)
  • 23-introduction_to_data_frames.mkv (172.1 MB)
  • 54-two-variable_plots.mkv (170.6 MB)
  • 36-functional_programming.mkv (169.1 MB)
  • 37-data_importexport.mkv (164.5 MB)
  • 48-string_manipulation.mkv (152.8 MB)
  • 21-creating_matrices.mkv (152.3 MB)
  • 24-creating_data_frames.mkv (146.8 MB)
  • 71-logistic_regression_intro.mkv (142.5 MB)
  • 59-a_basic_webapp.mkv (132.1 MB)
  • 02-what_is_data_science.mkv (119.8 MB)
  • 69-linear_regression_a_real_model_intro.mkv (108.2 MB)
  • 50-json_parsing.mkv (103.9 MB)
  • 07-getting_started_with_r.mkv (102.8 MB)
  • 15-vectors_part_one.mkv (100.9 MB)
  • 32-functions.mkv (100.7 MB)
  • 33-packages.mkv (96.7 MB)
  • 56-styling_and_saving.mkv (87.2 MB)
  • 17-vectors_missing_values.mkv (86.1 MB)
  • 09-r_files.mkv (84.8 MB)
  • 28-relational_operators.mkv (82.8 MB)
  • 45-the_arrange_verb.mkv (79.6 MB)
  • 30-conditional_statements.mkv (74.1 MB)
  • 03-machine_learning_overview.mkv (70.5 MB)
  • 61-intro_to_machine_learning___part_1.mkv (68.4 MB)
  • 40-tidy_data.mkv (68.0 MB)
  • 04-data_science_and_machine_learning_marketplace.mkv (57.3 MB)
  • 75-getting_started_with_freelancing.mkv (56.6 MB)
  • 51-data_visualization_in_r_intro.mkv (54.9 MB)
  • 29-logical_operators.mkv (54.8 MB)
  • 06-data_science_job_roles.mkv (51.8 MB)
  • 18-vectors_coercion.mkv (51.1 MB)
  • 11-r_studio.mkv (49.4 MB)
  • 74-creating_a_data_science_resume.mkv (46.7 MB)
  • 08-r_basics.mkv (45.6 MB)
  • 12-r_resources.mkv (45.5 MB)
  • 41-the_pipe_operator.mkv (44.6 MB)
  • 14-basic_types.mkv (42.7 MB)
  • 10-r_tidyverse.mkv (38.6 MB)
  • 19-vectors_naming.mkv (38.4 MB)
  • 31-loops.mkv (37.8 MB)
  • 01-data_science_and_ml_with_r_course_overview.mkv (35.7 MB)
  • 05-dsand_ml_job_opportunities_.mkv (30.9 MB)
  • 73-starting_a_data_science_career.mkv (28.3 MB)
  • 20-vectors_misc..mkv (24.1 MB)
  • skillshare.data.science.and.machine.learning.with.r.masterclass-skilledhares-sample.mkv (8.5 MB)
  • skillshare.data.science.and.machine.learning.with.r.masterclass-skilledhares.nfo (1.7 KB)

Code:

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