R-tree

R programming for Beginners | R programming for data Science

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. This video is a

From playlist Programming

Linear Regression Using R

How to calculate Linear Regression using R. http://www.MyBookSucks.Com/R/Linear_Regression.R http://www.MyBookSucks.Com/R Playlist http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C

From playlist Linear Regression.

Introduction to R: Decision Trees

This lesson covers the basics of decision trees in R. This is lesson 29 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below: Intro to R: Decision Trees https://www.kaggle.com/hamelg/intro-to-r-part-29-Decision

From playlist Introduction to R

1.6 Arrays and matrices in R | statistical analysis and data science course Rstudio | Dimensional

In this chapter of the video series in the crash course in statistics and data science with R / Rstudio we will see the definition, utilization, and importance of arrays with R. Also, we discuss their extension from vectors to matrices. Part 1: Definition - What is an array? - Array or

From playlist R Tutorial | Rstudio

Tree Graphs - Intro to Algorithms

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

From playlist Introduction to Algorithms

Scatter plots using Plotly for R

This videos show the creation of scatter plots using Plotly for the R programming language. The files are available online. R-markdown: https://github.com/juanklopper/Plotly-for-R RPubs: http://rpubs.com/juanhklopper/scatter_plots_using_plotly

From playlist Statistics

Introduction to R: Vectors

In this lesson we learn about the most basic compound data type in R: the vector. Vectors in R are essentially lists of values of the same basic data type. R vectors are great for data analytics and data science because many common functions are built to operate on entire vectors all at on

From playlist Introduction to R

Introduction to R: Random Forests

This lesson covers the basics of random forests in R. This is lesson 30 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below: Intro to R: Random Forests https://www.kaggle.com/hamelg/intro-to-r-part-30-Random-F

From playlist Introduction to R

Finding the Tallest Tree: comparing tree-based models

Tree-based models such as decision trees, random forests, and boosted trees provide powerful predictions and are fast to compute. There are many different ways to fit these models in R, including the rpart, randomForest, and xgboost packages. During this talk, we'll examine numerous ways t

From playlist Introduction to Machine Learning

Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machine Learning |Simplilearn

🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=DecisionTreeInR-HmEPCEXn-ZM&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: http

From playlist Data Science For Beginners | Data Science Tutorial🔥[2022 Updated]

Stanford Lecture: Don Knuth—"The Associative Law, or the Anatomy of Rotations in Binary Trees"

First Annual Christmas Lecture November 30, 1993 Professor Knuth is the Professor Emeritus at Stanford University. Dr. Knuth's classic programming texts include his seminal work The Art of Computer Programming, Volumes 1-3, widely considered to be among the best scientific writings of the

From playlist Donald Knuth Lectures

Lec 5 | MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503), Fall 2005

Lecture 05: Linear-time Sorting: Lower Bounds, Counting Sort, Radix Sort View the complete course at: http://ocw.mit.edu/6-046JF05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503),

Meltem Ünel: Height coupled trees

HYBRID EVENT Recorded during the meeting "Random Geometry" the January 20, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics

From playlist Probability and Statistics

(ML 2.4) Growing a classification tree (CART)

How to build a decision tree for classification using the CART approach. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

Gradient Boost Part 2 (of 4): Regression Details

Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the second part in a series that walks through it one step at a time. This video focuses on the original Gradient Boost algorithm used to predict a continuou

From playlist StatQuest

Proof: Euler's Formula for Plane Graphs | Graph Theory

We'll be proving Euler's theorem for connected plane graphs in today's graph theory lesson! Commonly know by the equation v-e+f=2, or in more common graph theory notation n-m+r=2, we'll prove this famous result using a minimum counterexample proof! The result states that, for connected pl

From playlist Graph Theory

Introduction to R: Matrices

In this lesson we learn about matrices: two-dimensional data structures in R with rows and columns. Matrices are a building block to learning about more complicated tabular data structures like data frames which are used extensively in data science. This is lesson 6 of a 30-part introduct

From playlist Introduction to R

Intro to Machine Learning: Lesson 2

Random Forest Deep Dive. Today we start by learning about metrics, loss functions, and (perhaps the most important machine learning concept) overfitting. We discuss using validation and test sets to help us measure overfitting. Then we'll learn how random forests work - first, by looking

From playlist Introduction to Machine Learning for Coders