Advanced Data Mining projects with R
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Advanced Data Mining projects with R

Discover the versatility of R for data mining with this collection of real-world dataset analysis techniques
1.0 (2 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
27 students enrolled
Created by Packt Publishing
Last updated 4/2017
Current price: $10 Original price: $125 Discount: 92% off
5 hours left at this price!
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  • 1.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Create predictive models in order to build a recommendation engine
  • Implement various dimension reduction techniques to handle large datasets
  • Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining
View Curriculum
  • They should have prior knowledge of basic statistics and some experience with the basic data mining techniques and algorithms.

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.

Who is the target audience?
  • Data analysts and data scientists with some knowledge of R, who need a helping hand in developing complex data mining projects are the ideal audience for this video course.
Compare to Other R Courses
Curriculum For This Course
17 Lectures
Clustering with E-commerce Data
4 Lectures 28:51

This video provides an overview of the entire course.

Preview 03:53

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.

Understanding Customer Segmentation

There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.

Clustering Methods – K means and Hierarchical

In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.

Clustering Methods – Model Based, Other and Comparison
Building a Retail Recommendation Engine
3 Lectures 14:40

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.

Preview 07:29

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.

Application of Methods and Limitations of Collaborative Filtering

As we are armed with the theory of recommendation, we will now build a recommendation engine.

Practical Project
Dimensionality Reduction
3 Lectures 24:37

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.

Preview 09:14

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.

Practical Project around Dimensionality Reduction

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.

Parametric Approach to Dimension Reduction
Applying Neural Network to Healthcare Data
7 Lectures 16:36

Before working on neural networks, we need to understand the theory behind neural networks.

Preview 04:07

To understand and implement the neural networks, we need to understand the maths behind it. This video will do just that!

Understanding the Math Behind the Neural Network

After knowing about neural networks, we need to see how to implement neural networks in R.

Neural Network Implementation 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.

Neural Networks for Prediction

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.

Neural Networks for Classification

We will also perform forecasting using neural networks. In this video, we will forecast a time series.

Neural Networks for Forecasting

After working with neural networks, we should also know the merits and demerits of the famous technology.

Merits and Demerits of Neural Networks
About the Instructor
Packt Publishing
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