R Fundamentals, Information Science, Statistical Machine Studying fashions, Deep Studying, Shiny and rather more (All R code included)
What you’ll study
study all points of R from Fundamentals, over Information Science, to Machine Studying and Deep Studying
study R fundamentals (information sorts, constructions, variables, …)
study R programming (writing loops, features, …)
information im- and export
fundamental information manipulation (piping, filtering, aggregation of outcomes, information reshaping, set operations, becoming a member of datasets)
information visualisation (completely 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 methods?)
regularization (What’s regularization? How will you apply it?)
classification fashions (perceive completely different algorithms and learn to apply logistic regression, determination timber, random forests, help 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 net software improvement and deployment
Description
You need to have the ability to carry out your individual information analyses with R? You wish to learn to get business-critical insights out of your information? Otherwise you wish to get a job on this superb discipline? In all of those instances, you discovered the suitable course!
We’ll begin with the very Fundamentals of R, like information sorts and -structures, programming of loops and features, information im- and export.
Then we are going to dive deeper into information evaluation: we are going to learn to manipulate information by filtering, aggregating outcomes, reshaping information, set operations, and becoming a member of datasets. We’ll uncover completely different visualisation methods for presenting advanced information. Moreover discover out to current interactive timeseries information, or interactive geospatial information.
Superior information manipulation methods are lined, e.g. outlier detection, lacking information dealing with, and common expressions.
We’ll cowl all fields of Machine Studying: Regression and Classification methods, Clustering, Affiliation Guidelines, Reinforcement Studying, and, presumably most significantly, Deep Studying for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, …
Additionally, you will study to develop net purposes and learn how to deploy them with R/Shiny.
For every discipline, completely different algorithms are proven intimately: their core ideas are offered in 101 classes. Right here, you’ll perceive how the algorithm works. Then we implement it collectively in lab classes. We develop code, earlier than I encourage you to work on train by yourself, earlier than you watch my answer examples. With this information you may 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 learn how to take your information into the true world.
You’re going to get entry to an interactive studying platform that can enable you to 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 essential 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 danger for you taking the course proper now. Don’t wait. See you within the course.
Content material
Course Introduction
Information Varieties and -structures
R Programming
Information Im- and Export
Primary Information Manipulation
Information Visualisation
Superior Information Manipulation
Machine Studying: Introduction
Machine Studying: Regression
Machine Studying: Mannequin Preparation and Analysis
Machine Studying: Regularization
Machine Studying: Classification Fundamentals
Machine Studying: Classification with Determination Timber
Machine Studying: Classification with Random Forests
Machine Studying: Classification with Logistic Regression
Machine Studying: Classification with Help Vector Machines
Machine Studying: Classification with Ensemble Fashions
Machine Studying: Affiliation Guidelines
Machine Studying: Clustering
Machine Studying: Dimensionality Discount
Machine Studying: Reinforcement Studying
Deep Studying: Introduction
Deep Studying: Regression
Deep Studying: Classification
Deep Studying: Convolutional Neural Networks
Deep Studying: Autoencoders
Deep Studying: Switch Studying and Pretrained Networks
Deep Studying: Recurrent Neural Networks
Bonus
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