Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It can be used for day-to-day data analysis tasks.
Data mining is a very broad topic and takes some time to learn. This course will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This course covers each and every aspect of data mining in order to prepare you for real-world problems. You'll come to understand the different disciplines in data mining. In every discipline, there exist a variety of different algorithms. At least one algorithm of the various classes of algorithms will be covered to give you a foundation to further apply your knowledge to dive deeper into the different flavors of algorithms.
After completing this course, you will be able to solve real-world data mining problems.
About The Author
Romeo Kienzler is a Chief Data Scientist at the IBM Watson IoT Division. In his role, he is involved in international data mining and data science projects to ensure that clients get the most out of their data. He works as an Associate Professor for data mining at a Swiss University and his current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J. He also contributes to various open source projects. Additionally, he is currently writing a chapter on Hyperledger for a book on Blockchain technologies.
The aim of this video is to show how easy it is to use R for data mining. On the other hand, the expectations are set because R is sometimes a bit hard to learn—especially for programmers.
You have to accept that most of your work will involve data cleansing, which is one of the most important steps in data mining. Fortunately, R has all the tools in place to do this task as elegantly as possible.
The aim of this video is to explain the basic concepts of R. This is exemplified by showing how easy it is to load data in R. Get an idea about how this is done in most of the cases as well as for some special cases such as databases and big data technologies.
This video gives an overview of the data frame object, which is an essential part of R and part of every analysis. You will learn what a data frame is and how to use it for data manipulation.
We want to explain that data is nothing but points in a multidimensional vector space exemplified by an example.
Points in a multidimensional vector space can be drawn and analyzed by introducing k-means—the simplest of the clustering algorithms.
Coming from a hard-to-understand dataset, process and visualize it to gain insights.
The aim of this video is to show how powerful R is as a data language. We will query an internal example dataset and show how it can be filtered and aggregated on.
The aim of this video is to show how powerful R is as a data language. Now we concentrate on data types.
Next, we concentrate on functions and indexing.
The aim of this video is to show how object-oriented programming is done in R since some of the algorithms covered rely on it.
The aim of this video is to show a little example to motivate the attendee based on the standard market basket analysis.
The aim of this video is to explain the mathematical structure "graph".
The aim of this video is to explain the different types of association rules.
The aim of this video is to explain the Apriori Algorithm.
The aim of this video is to explain the Eclat Algorithm.
The aim of this video is to explain the FP-Growth Algorithm.
This video introduces the discipline of classification, the mathematical foundation for understanding Bayes' theorem and the Naïve Bayes classifier.
Now since we've understood Bayes' theorem, we can derive the Bayes classifier and use naïve Bayes for spam classification in R.
This is a practical example of using naïve Bayes for spam classification in R
Introduction to support vector machines, understanding how to use them to separate points in multidimensional vector spaces, and finally using kernels in non-linearly separable data
Introduction to lazy learning using k-nearest neighbors. This video explains how KNNs work and how they are applied in R
This video introduces the discipline of hierarchical clustering.
This video introduces the discipline of distribution based clustering.
This video introduces the discipline of density based clustering.
A practical example of using DBSCAN in R.
This video introduces neural networks.
This video shows an example in R—how to use the H2D deep learning framework for handwritten digit recognition (classification).
This video shows an example in R—how to use the H2D deep learning framework for anomaly detection of real-time Iot sensor data.
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