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.
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 Choice Bushes
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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