Mario Castro | Poke

Mario Castro @UCKacOPkhUImP-pFWRo8wx6A@youtube.com

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SARIMA Identification Models in R: Part 1

SEASONAL AutoRegressive Integrated Moving Average a.k.a SARIMA(p,d,q)(P,D,Q)

AutoRegressive Integrated Moving Average: a.k.a ARIMA(p,d,q)

Generating and fitting ARMA processes

ARMA Model Fitting and Diagnosis

ARMA Model Identification

Auto-regressive and Moving Average models: AR(p) + MA(q)

Introduction to ARIMA-like models: the humble Random Walk

Forecasting: Decomposition methods (part I)

Forecasting: Exponential smoothing methods

A tutorial on time series decomposition in R

Forecasting: Decomposition methods (part II: Advanced methods)

A very basic look at subsetting and residuals with R

Playing with time series and simple forecasts with R

Forecasting: Transforming the data

Forecasting: Super simple methods and the library "forecast"

Introduction to forecasting

Super easy GAM regression using ggplot2

Non-parametric models part III: Comparison of GAMs

Non-parametric models part II: Polynomial splines

Non-parametric models part I: Generalized Additive Models

Linear Model Selection (for regression) part IV: Dimensionalty reduction methods

Linear Model Selection (for regression) part III: Ridge regression and the LASSO

Linear Model Selection (for regression) part II: Brute force

Linear Model Selection (for regression) part I: Motivation and definitions

Beyond linear regression: interactions (part II)

Beyond linear regression: interactions (part I)

Introduction to linear regression part IV: Simple polynomial regression

Introduction to linear regression part III: Categorical regressors

Linear regression in R: checking your fit (losers don’t do it)

Linear regression in R: super simple examples and motivation

Linear regression part II: What's the meaning of correlation?

Introduction to linear regression part I: Definitions and ideas

A brief introduction to regression

Entrevista Aragón radio octubre 2020

Automatic Feature selection part II: Let's code in R

Automatic Feature selection part I: Some theory and general ideas

Variable importance: Less is more

Training networks part III: Backpropagation, overfitting and all that jazz

Training networks part II: The ideas behind gradient descent

Training networks part I: The problem of overfitting

Exploring Neural Network performance in R with caret

Basic Neural Networks in R with caret

Introduction to neural networks III: Cooperation among neurons makes possible the impossible

Introduction to neural networks II: From neurons to networks

Introduction to neural networks I: Neurons are overrated

The wrong question to ask: What's the best classification algorithm?

Classification: Support Vector Machines (part 2)

Classification: Support Vector Machines (part 1)

Random Forests with caret: Accuracy and variable importance

Random forests: From decision trees to "shuffled" collections of trees

Decision trees with caret and “rpart”

Classification And Regression Trees

Cross validation in R with the caret library

Logistic regression in R with the glm function (and some mentions to caret)

The Caret package: your new best friend

Validation (and Cross-validation)

Una super-tutoría que resume toda la cinemática de la partícula

k-Nearest neighbors in R with the "class" package

Classification: Logistic regression