Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario.
About the Authors
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.
If you have any questions, don't hesitate to look him up on Twitter via @mishra1_PK—he will be more than glad to help a fellow web professional wherever, whenever.
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.
Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.
With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.
From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.
Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.