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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 is very useful for day-to-day data analysis tasks.
Data mining is a very broad topic and takes some time to learn. This Learning Path will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This Learning Path explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields.
This Learning Path is the complete learning process for data-happy people. We begin with a thorough introduction to data mining and how R makes it easy with its many packages. We then move on to exploring data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields using R’s vast set of algorithms.
The goal of this Learning Path is to help you understand the basics of data mining with R and then get you working on real-world datasets and projects.
This Learning Path is authored by some of the best in their fields.
Romeo Kienzler
Romeo Kienzler is the Chief Data Scientist of the IBM Watson IoT Division and working as an Advisory Architect helping client worldwide to solve their data analysis problems.
He holds an M. Sc. of Information System, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology. 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.
Pradeepta Mishra
Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and econometrician. He currently leads the data science and machine learning practice for Ma Foi Analytics, Bangalore, India. Ma Foi Analytics is an advanced analytics provider for Tomorrow's Cognitive Insights Ecology, using a combination of cutting-edge artificial intelligence, a proprietary big data platform, and data science expertise. He holds a patent for enhancing the planogram design for the retail industry. Pradeepta has published and presented research papers at IIM Ahmedabad, India. He is a visiting faculty member at various leading B-schools and regularly gives talks on data science and machine learning.
Pradeepta has spent more than 10 years solving various projects relating to classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures, spanning across domains such as healthcare, insurance, retail and e-commerce, manufacturing, and so on.
This video will provide you an overview of entire course.
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.
This video provides an overview of the entire course.
The process of deciphering meaningful insights from existing databases and analyzing results for consumption by business users.
We are going to start with basic programming using R for data management and data manipulation.
Changing one data type to another if the formatting is not done properly is not difficult at all using R.
While working on a client dataset with a large number of observations, it is required to subset the data based on some selection criteria and with or without replacement-based sampling.
The date functions return a Date class that represents the number of days since January 1, 1970.
There are two different types of functions in R, user-defined functions and built-in Functions.
Using a loop, a similar task can be performed many times.
The apply function uses an array, a matrix, or a dataframe as an input and returns the result in an array format.
In typical data management, it is important to standardize the text columns or variables in a dataset because R is case sensitive and it reads any discrepancy as a new data point.
The R programming language, missing values are represented as NA. NAs are not string or numeric values; they are considered as an indicator for missing values.
To generate univariate statistics about a dataset, we have to follow two approaches, one for continuous variables and the other for discrete or categorical variables.
The relationship or association between two variables is known as bivariate analysis. There are three possible ways of looking at the relationship.
The multivariate relationship is a statistical way of looking at multiple dependent and independent variables and their relationships.
Understanding probability distributions is important in order to have a clear idea about the assumptions of any statistical hypothesis test.
Interpretation of the calculated distribution helps in forming a hypothesis.
Contingency tables are frequency tables represented by two or more categorical variables Frequency table is used to represent one categorical variable; however, contingency table is used to represent two categorical variables.
The null hypothesis states that nothing has happened; the means are constant, and so on. However, the alternative hypothesis states that something different has happened and the means are different about a population.
When a training dataset does not conform to any specific probability distribution because of non-adherence to the assumptions of that specific probability distribution, the only option left to analyze the data is via non-parametric methods.
This video will walk you through the basics of data visualization along with how to create advanced data visualization using existing libraries in R programming language.
This video will let you explore different kinds of charts and plots and their creation. You'll also be able to use geo mapping.
By the end of this video, you will be able to use some amazing data visualization techniques which are widely used for smart Data representation.
This video will teach you how to take the plotting to a new level. Here, you will learn to use the plotly library, which is designed as an interactive browser-based charting library built on the JavaScript library.
This video will let you explore the Geo mapping which is a type of chart, used by data mining experts when the dataset contains location information.
How could you predict the future outcomes of a target variable? Regression is the answer to this. Let's have a brief introduction and understand regression.
This video will let you explore about Linear regression model which can be used for explaining the relationship between a single dependent variable and independent variable.
This video will let you understand the use of stepwise regression method to solve complex regression problems.
What could we do in those scenarios where the variable of interest is categorical in nature, such as buying a product or not, approving a credit card or not, tumor is cancerous or not, and so on? Logistic regression is the best solution to these.
Let's dive into another form of regression where the parameters in a linear regression model are increased up to one or two levels of polynomial calculation.
Market Basket Analysis is the study of relationships between various products and products that are purchased together or in a series of transactions.
Implementing market basket analysis.
This video provides an overview of the entire course.
It is important to classify objects according to their similarities or dissimilarities so that their study becomes easier. We use clustering techniques for that purpose.
There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
Recommendation is a technique by which the algorithm detects what the user is buying. You would always like to be recommended things similar to your interest or things you have bought before. Recommendation engine helps in doing that.
There are different types of methods for building recommendation engine. You need to know which method to use depending on what type of product shopping you do. Also, there are certain limitations to these methods.
As we are armed with the theory of recommendation, we will now build a recommendation engine.
When there are a lot of variables, it becomes difficult to extract data. We need to devise something that will let us gather data in less number of variables. Dimensionality reduction provides you with that solution.
In order to understand dimensionality reduction, we need to work with it. Here, we will apply dimensionality reduction procedure, both the model-based and principal component-based approaches.
We can also try some other approaches to perform dimensionality reduction according to the need of the dataset. Let's look at that in this video.
Before working on neural networks, we need to understand the theory behind neural networks.
To understand and implement the neural networks, we need to understand the maths behind it. This video will do just that!
After knowing about neural networks, we need to see how to implement neural networks in R.
Prediction is an important aspect of data mining. In this video, we will create a prediction model using neural network to predict the auction average price.
We need to form clusters or groups of data so that performing actions on them becomes easier. Here we are going to classify customers based on marketing.
We will also perform forecasting using neural networks. In this video, we will forecast a time series.
After working with neural networks, we should also know the merits and demerits of the famous technology.
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