R Ultimate 2024: R for Data Science and Machine Learning

Destiny For Everything


R Fundamentals, Information Science, Statistical Machine Studying fashions, Deep Studying, Shiny and rather more (All R code included)

What you’ll study

study all elements of R from Fundamentals, over Information Science, to Machine Studying and Deep Studying

study R fundamentals (information varieties, buildings, variables, …)

study R programming (writing loops, capabilities, …)

information im- and export

fundamental information manipulation (piping, filtering, aggregation of outcomes, information reshaping, set operations, becoming a member of datasets)

information visualisation (totally different packages are realized, e.g. ggplot, plotly, leaflet, dygraphs)

superior information manipulation (outlier detection, lacking information dealing with, common expressions)

regression fashions (create and apply regression fashions)

mannequin analysis (What’s underfitting and overfitting? Why is information splitted into coaching and testing? What are resampling strategies?)

regularization (What’s regularization? How are you going to apply it?)

classification fashions (perceive totally different algorithms and learn to apply logistic regression, resolution bushes, random forests, assist vector machines)

affiliation guidelines (study the apriori mannequin)

clustering (kmeans, hierarchical clustering, DBscan)

dimensionality discount (issue evaluation, principal element evaluation)

Reinforcement Studying (higher confidence certain)

Deep Studying (deep studying for multi-target regression, binary and multi-label classification)

Deep Studying (study picture classification with convolutional neural networks)

Deep Studying (find out about Semantic Segmentation)

Deep Studying (Recurrent Neural Networks, LSTMs)

Extra on Deep Studying, e.g. Autoencoders, pretrained fashions, …

R/Shiny for internet software growth and deployment

Description

You need to have the ability to carry out your individual information analyses with R? You need to learn to get business-critical insights out of your information? Otherwise you need to get a job on this superb subject? In all of those circumstances, you discovered the precise course!

We’ll begin with the very Fundamentals of R, like information varieties and -structures, programming of loops and capabilities, information im- and export.

Then we’ll dive deeper into information evaluation: we’ll learn to manipulate information by filtering, aggregating outcomes, reshaping information, set operations, and becoming a member of datasets. We’ll uncover totally different visualisation strategies for presenting complicated information. Moreover discover out to current interactive timeseries information, or interactive geospatial information.

Superior information manipulation strategies are coated, e.g. outlier detection, lacking information dealing with, and common expressions.

We’ll cowl all fields of Machine Studying: Regression and Classification strategies, Clustering, Affiliation Guidelines, Reinforcement Studying, and, probably most significantly, Deep Studying for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, …

Additionally, you will study to develop internet functions and the way to deploy them with R/Shiny.

For every subject, totally different algorithms are proven intimately: their core ideas are offered in 101 periods. Right here, you’ll perceive how the algorithm works. Then we implement it collectively in lab periods. We develop code, earlier than I encourage you to work on train by yourself, earlier than you watch my resolution examples. With this data you possibly can clearly establish an issue at hand and develop a plan of assault to unravel it.

You’ll perceive the benefits and downsides of various fashions and when to make use of which one. Moreover, you’ll know the way to take your data into the actual world.

You’re going to get entry to an interactive studying platform that may aid you to know the ideas significantly better.

On this course code won’t ever come out of skinny air by way of copy/paste. We’ll develop each necessary line of code collectively and I’ll inform you why and the way we implement it.

Check out some pattern lectures. Or go to a few of my interactive studying boards. Moreover, there’s a 30 day a refund guarantee, so there isn’t any threat for you taking the course proper now. Don’t wait. See you within the course.

English
language

Content material

Course Introduction

Course Overview
R and RStudio (Overview and Set up)
get the code
RStudio Introduction / Challenge Setup
File Codecs
Rmarkdown Lab
Bundle Dealing with

Information Varieties and -structures

Primary Information Varieties 101
Primary Information Varieties Lab
Matrices and Arrays Lab
Lists
Components
Dataframes
Strings Lab
Datetime

R Programming

Operators
Loops 101
Loops Lab
Capabilities 101
Capabilities Lab (Intro)
Capabilities Lab (Coding)

Information Im- and Export

Information Import Lab
Information Export Lab
Net Scraping Intro
Net Scraping Lab

Primary Information Manipulation

Piping 101
Filtering 101
Filtering Lab
Filtering Train
Filtering Resolution
Information Aggregation 101
Information Aggregation Lab
Information Aggregation Train
Information Aggregation Resolution
Information Reshaping 101
Information Reshaping Lab
Information Reshaping Train
Information Reshaping Resolution
Set Operations 101
Set Operations Lab
Becoming a member of Datasets 101
Becoming a member of Datasets Lab

Information Visualisation

Visualisation Overview
ggplot 101
ggplot Lab
plotly Lab (Intro)
plotly Lab
leaflet Lab (Intro)
leaflet Lab
dygraphs Lab (Intro)
dygraphs Lab

Superior Information Manipulation

Outlier Detection 101
Outlier Detection Lab (Intro)
Outlier Detection Lab
Outlier Detection Train
Outlier Detection Resolution
Lacking Information Dealing with 101
Lacking Information Dealing with Lab (Intro)
Lacking Information Dealing with Lab (1/1)
Common Expressions 101
Common Expressions Lab

Machine Studying: Introduction

AI 101
Machine Studying 101
Fashions

Machine Studying: Regression

Regression Varieties 101
Univariate Regression 101
Univariate Regression Interactive
Univariate Regression Lab
Univariate Regression Train
Univariate Regression Resolution
Polynomial Regression 101
Polynomial Regression Lab
Multivariate Regression 101
Multivariate Regression Lab
Multivariate Regression Train
Multivariate Regression Resolution

Machine Studying: Mannequin Preparation and Analysis

Underfitting / Overfitting 101
Practice / Validation / Take a look at Break up 101
Practice / Validation / Take a look at Break up Interactive
Practice / Validation / Take a look at Break up Lab
Resampling Methods 101
Resampling Methods Lab

Machine Studying: Regularization

Regularization 101
Regularization Lab

Machine Studying: Classification Fundamentals

Confusion Matrix 101
ROC Curve 101
ROC Curve Interactive
ROC Curve Lab Intro
ROC Curve Lab 1/3 (Information Prep, Modeling)
ROC Curve Lab 2/3 (Confusion Matrix and ROC)
ROC Curve Lab 3/3 (ROC, AUC, Value Operate)

Machine Studying: Classification with Choice Bushes

Choice Bushes 101
Choice Bushes Lab (Intro)
Choice Bushes Lab (Coding)
Choice Bushes Train
Choice Bushes Resolution

Machine Studying: Classification with Random Forests

Random Forests 101
Random Forests Interactive
Random Forest Lab (Intro)
Random Forest Lab (Coding 1/2)
Random Forest Lab (Coding 2/2)

Machine Studying: Classification with Logistic Regression

Logistic Regression 101
Logistic Regression Lab (Intro)
Logistic Regression Lab (Coding 1/2)
Logistic Regression Lab (Coding 2/2)
Logistic Regression Train
Logistic Regression Resolution

Machine Studying: Classification with Help Vector Machines

Help Vector Machines 101
Help Vector Machines Lab (Intro)
Help Vector Machines Lab (Coding 1/2)
Help Vector Machines Lab (Coding 2/2)
Help Vector Machines Train

Machine Studying: Classification with Ensemble Fashions

Ensemble Fashions 101

Machine Studying: Affiliation Guidelines

Affiliation Guidelines 101
Apriori 101
Apriori Lab (Intro)
Apriori Lab (Coding 1/2)
Apriori Lab (Coding 2/2)
Apriori Train
Apriori Resolution

Machine Studying: Clustering

Clustering Overview
kmeans 101
kmeans Lab
kmeans Train
kmeans Resolution
Hierarchical Clustering 101
Hierarchical Clustering Interactive
Hierarchical Clustering Lab
Dbscan 101
Dbscan Lab

Machine Studying: Dimensionality Discount

PCA 101
PCA Lab
PCA Train
PCA Resolution
t-SNE 101
t-SNE Lab (Sphere)
t-SNE Lab (Mnist)
Issue Evaluation 101
Issue Evaluation Lab (Intro)
Issue Evaluation Lab (Coding 1/2)
Issue Evaluation Lab (Coding 2/2)
Issue Evaluation Train

Machine Studying: Reinforcement Studying

Reinforcement Studying 101
Higher Confidence Sure 101
Higher Confidence Sure Interactive
Higher Confidence Sure Lab (Intro)
Higher Confidence Sure Lab (Coding 1/2)
Higher Confidence Sure Lab (Coding 2/2)

Deep Studying: Introduction

Deep Studying Normal Overview
Deep Studying Modeling 101
Efficiency
From Perceptron to Neural Networks
Layer Varieties
Activation Capabilities
Loss Operate
Optimizer
Deep Studying Frameworks
Python and Keras Set up

Deep Studying: Regression

Multi-Goal Regression Lab (Intro)
Multi-Goal Regression Lab (Coding 1/2)
Multi-Goal Regression Lab (Coding 2/2)

Deep Studying: Classification

Binary Classification Lab (Intro)
Binary Classification Lab (Coding 1/2)
Binary Classification Lab (Coding 2/2)
Multi-Label Classification Lab (Intro)
Multi-Label Classification Lab (Coding 1/3)
Multi-Label Classification Lab (Coding 2/3)
Multi-Label Classification Lab (Coding 3/3)

Deep Studying: Convolutional Neural Networks

Convolutional Neural Networks 101
Convolutional Neural Networks Interactive
Convolutional Neural Networks Lab (Intro)
Convolutional Neural Networks Lab (1/1)
Convolutional Neural Networks Train
Convolutional Neural Networks Resolution
Semantic Segmentation 101
Semantic Segmentation Lab (Intro)
Semantic Segmentation Lab (1/1)

Deep Studying: Autoencoders

Autoencoders 101
Autoencoders Lab (Intro)
Autoencoders Lab (Coding)

Deep Studying: Switch Studying and Pretrained Networks

Switch Studying and Pretrained Fashions 101
Switch Studying and Pretrained Fashions Lab (Intro)
Switch Studying and Pretrained Fashions Lab (1/1)

Deep Studying: Recurrent Neural Networks

Recurrent Neural Networks 101
LSTM: Univariate, Multistep Timeseries Prediction (Intro)
LSTM: Univariate, Multistep Timeseries Prediction Lab (1/1)
LSTM: Multivariate, Multistep Timeseries Prediction (Intro)
LSTM: Multivariate, Multistep Timeseries Prediction Lab (1/1)

Bonus

Congratulations and thanks

The post R Final 2024: R for Information Science and Machine Studying appeared first on destinforeverything.com.

Please Wait 10 Sec After Clicking the "Enroll For Free" button.