24h Pro data science in R

24h Pro data science in R, available at $49.99, has an average rating of 3.7, with 75 lectures, 4 quizzes, based on 26 reviews, and has 262 subscribers.

You will learn about Do machine learning in R Process data for modelling This course is ideal for individuals who are Students aiming to do serious data science in R, with some knowledge about statistics It is particularly useful for Students aiming to do serious data science in R, with some knowledge about statistics.

Enroll now: 24h Pro data science in R

Summary

Title: 24h Pro data science in R

Price: $49.99

Average Rating: 3.7

Number of Lectures: 75

Number of Quizzes: 4

Number of Published Lectures: 75

Number of Published Quizzes: 4

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: ￡44.99

Quality Status: approved

Status: Live

What You Will Learn

Do machine learning in R

Process data for modelling

Who Should Attend

Students aiming to do serious data science in R, with some knowledge about statistics

Target Audiences

Students aiming to do serious data science in R, with some knowledge about statistics

This course explores several modern?machine learning and data science techniques in R. As you probably know, R is one of the most used tools among data scientists. We showcase a wide array of statistical and machine learning techniques. In particular:

Using R’s statistical functions for drawing random numbers, calculating densities, histograms, etc.

Supervised ML problems using the CARET package

Data processing using sqldf, caret, etc.

Unsupervised techniques such as PCA, DBSCAN, K-means

Calling Deep Learning models in Keras(Python) from R

Use the powerful XGBOOST method for both regression and classification

Doing interesting plots, such as geo-heatmaps and interactive plots

Train ML train?hyperparameters for several ML methods using caret

Do linear regression in R, build log-log models, and do ANOVA analysis

Estimate mixed effects models to explicitly model the covariances between observations

Train outlier?robust models using robust regression and quantile regression

Identify outliers and novel observations

Estimate ARIMA (time series) models to predict temporal variables

Most of the examples presented in this course come from real datasets collected from the web such as Kaggle, the US Census Bureau, etc. All the lectures can be downloaded and come with the corresponding material. The teaching approach is to briefly introduce each technique, and focus on the computational aspect. The mathematical formulas are avoided as much as possible, so as to concentrate on the practical implementations.

This course covers most of what you would need to work as a data scientist, or compete in Kaggle competitions. It is assumed that you already have some exposure to data science / statistics.?

Course Curriculum

Chapter 1: Basics

Lecture 1: Introduction

Lecture 2: Setting up R

Chapter 2: General R programming

Lecture 1: The data frame

Lecture 2: Variables

Lecture 3: Reading data

Lecture 4: Reading data with dates: Classes for customized dates

Lecture 5: Text

Lecture 6: Functions

Lecture 7: The apply family of functions

Lecture 8: Histograms

Chapter 3: Random numbers, probability and statistics

Lecture 1: Generating random numbers

Lecture 2: Density and cumulative distribution function

Lecture 3: Comparing distributions

Chapter 4: Advanced data processing using sqldf

Lecture 1: sqldf – Part1

Lecture 2: sqldf – Part2

Chapter 5: Statistical modelling: Linear regression

Lecture 1: Dummy variables

Lecture 2: The lm() function: Part1

Lecture 3: The lm function: Part2

Lecture 4: Comparing models

Lecture 5: Normality, residuals and transformations

Lecture 6: Log-log models

Lecture 7: Linear mixed effects models: Part1

Lecture 8: Linear mixed effects models: Part2

Lecture 9: Robust regression

Chapter 6: Statistical modelling: GLM and Nonlinear regression

Lecture 1: Logistic regression – Part1

Lecture 2: Logistic regression – Part 2

Lecture 3: Logistic regression – Part 3

Lecture 4: Logistic regression – Part 4

Lecture 5: Poisson regression: Part1

Lecture 6: Poisson regression: Part2

Lecture 7: Poisson regression: Part3

Lecture 8: Nonlinear regression

Chapter 7: XGBOOST: Gradient Boosting

Lecture 1: How does it work? Relevant parameters – Part1

Lecture 2: How does it work? Relevant parameters – Part2

Lecture 3: Using XGBoost for regression

Lecture 4: Cross validation in XGBOOST: the xgb.cv function

Lecture 5: GridSearch for XGBoost via the caret package

Lecture 6: Using XGBoost for classification

Chapter 8: Principal components

Lecture 1: Selecting PCA and projecting the data

Lecture 2: PCA regression

Chapter 9: Machine learning – the CARET package – introduction

Lecture 1: Introduction

Lecture 2: Preprocessing data: Part1

Lecture 3: Preprocessing data: Part2

Chapter 10: Sound

Lecture 1: Extracting meaningful sound features

Chapter 11: Machine learning – the CARET package – Supervised problems

Lecture 1: Introduction to train / finalModel vs train

Lecture 2: Naive Bayes

Lecture 3: Support Vector Machines (SVMs) – Part1

Lecture 4: Support Vector Machines (SVMs) – Part2

Lecture 5: Lasso, Ridge and Elasticnet

Lecture 6: GLMNet

Lecture 7: Extra trees

Lecture 8: Random Forests

Lecture 9: Bagged CARTS

Lecture 10: Oblique random forests

Lecture 11: Adaboost

Lecture 12: Stochastic gradient boosting

Lecture 13: Boosted Logistic Regression

Lecture 14: Multilayer Perceptron

Chapter 12: Unsupervised problems

Lecture 1: K-Means

Lecture 2: DBSCAN

Lecture 3: Novelty detection

Lecture 4: Outliers

Chapter 13: Deep learning / Neural networks via Keras in R

Lecture 1: Brief introduction to Python – Setting up Keras

Lecture 2: Using Keras – Layers: P1

Lecture 3: Using Keras – Layers : P2

Lecture 4: Calling Keras from R : Regression

Lecture 5: Neural Nets: Classification

Chapter 14: Time series in R

Lecture 1: Time series basics

Lecture 2: ACF and PACF

Lecture 3: The auto.arima package

Lecture 4: Predicting global temperatures using auto.arima

Lecture 5: Predicting the US GDP via auto.arima

Chapter 15: Visualizing data

Lecture 1: Geo data using Google Maps

Lecture 2: Interactive plots via the iPlots package

Chapter 16: Creating R packages

Lecture 1: Creating R packages

Instructors

Francisco Juretig
Mr

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 4 votes

4 stars: 7 votes

5 stars: 10 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!