Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
22 Hour(s) 42 Minute(s)
Language
English
Taught by
Bert Gollnick
Rating
3 views
Course Overview
R Basics, Data Science, Statistical Machine Learning models, Deep Learning, Shiny and much more (All R code included)
You want to be able to perform your own data analyses with R? You want to learn how to get business-critical insights out of your data? Or you want to get a job in this amazing field? In all of these cases, you found the right course!
We will start with the very Basics of R, like data types and -structures, programming of loops and functions, data im- and export.
Then we will dive deeper into data analysis: we will learn how to manipulate data by filtering, aggregating results, reshaping data, set operations, and joining datasets. We will discover different visualisation techniques for presenting complex data. Furthermore find out to present interactive timeseries data, or interactive geospatial data.
Advanced data manipulation techniques are covered, e.g. outlier detection, missing data handling, and regular expressions.
We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ...
You will also learn to develop web applications and how to deploy them with R/Shiny.
For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.
You will understand the advantages and disadvantages of different models and when to use which one. Furthermore, you will know how to take your knowledge into the real world.
You will get access to an interactive learning platform that will help you to understand the concepts much better.
In this course code will never come out of thin air via copy/paste. We will develop every important line of code together and I will tell you why and how we implement it.
Take a look at some sample lectures. Or visit some of my interactive learning boards. Furthermore, there is a 30 day money back warranty, so there is no risk for you taking the course right now. Don’t wait. See you in the course.
Course Content
- 10 section(s)
- 204 lecture(s)
- Section 1 Course Introduction
- Section 2 Data Types and -structures
- Section 3 R Programming
- Section 4 Data Im- and Export
- Section 5 Basic Data Manipulation
- Section 6 Data Visualisation
- Section 7 Advanced Data Manipulation
- Section 8 Machine Learning: Introduction
- Section 9 Machine Learning: Regression
- Section 10 Machine Learning: Model Preparation and Evaluation
What You’ll Learn
- learn all aspects of R from Basics, over Data Science, to Machine Learning and Deep Learning
- learn R basics (data types, structures, variables, and more)
- learn R programming (writing loops, functions, and more)
- data im- and export
- basic data manipulation (piping, filtering, aggregation of results, data reshaping, set operations, joining datasets)
- data visualisation (different packages are learned, e.g. ggplot, plotly, leaflet, dygraphs)
- advanced data manipulation (outlier detection, missing data handling, regular expressions)
- regression models (create and apply regression models)
- model evaluation (What is underfitting and overfitting? Why is data splitted into training and testing? What are resampling techniques?)
- regularization (What is regularization? How can you apply it?)
- classification models (understand different algorithms and learn how to apply logistic regression, decision trees, random forests, support vector machines)
- association rules (learn the apriori model)
- clustering (kmeans, hierarchical clustering, DBscan)
- dimensionality reduction (factor analysis, principal component analysis)
- Reinforcement Learning (upper confidence bound)
- Deep Learning (deep learning for multi-target regression, binary and multi-label classification)
- Deep Learning (learn image classification with convolutional neural networks)
- Deep Learning (learn about Semantic Segmentation)
- Deep Learning (Recurrent Neural Networks, LSTMs)
- More on Deep Learning, e.g. Autoencoders, pretrained models, ...
- R/Shiny for web application development and deployment
Reviews
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DDonal Kiernan
I was enjoying this course until the format was changed so a BETA version. I have tried reverting to the original classic version I purchased but there is no evident way to revert.
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HHumphrey Gachagua Muriuki
Good
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AAndres Mendez Solis
Several hours of watching code being written without any explaination of why any single command is chosen, how to use it beyond that particular case or its very meaning. It is not suitable for beginners in R and I'm not sure it's clear enough for those with little experience.
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JJuliet Nyantakyiwaa Addo
I am actually taking this course because i took the vourse as a car last semester in my global public health Masters program and i got lost in class. I wanted to get a grip of it well and i think i am making progress. Thank you
