Advanced Data Mining projects with R

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Description

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    Course

  • Methodology

    Online

  • Start date

    Different dates available

Discover the versatility of R for data mining with this collection of real-world dataset analysis techniques.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 AuthorPradeepta 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.

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Location

Start date

Online

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Different dates availableEnrolment now open

About this course

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

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This centre's achievements

2021

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The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 4 years

Subjects

  • Retail
  • Forecasting
  • Data Mining
  • E-commerce
  • Algorithms
  • Project
  • Healthcare
  • Database
  • Data Management
  • Information Systems

Course programme

Clustering with E-commerce Data 4 lectures 28:52 The Course Overview This video provides an overview of the entire course. Understanding Customer Segmentation 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.
  • Understand clustering techniques
  • Perform clustering techniques
Clustering Methods – K means and Hierarchical There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
Clustering Methods – Model Based, Other and Comparison In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
Clustering with E-commerce Data 4 lectures 28:52 The Course Overview This video provides an overview of the entire course. Understanding Customer Segmentation 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.
  • Understand clustering techniques
  • Perform clustering techniques
Clustering Methods – K means and Hierarchical There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
Clustering Methods – Model Based, Other and Comparison In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. This video provides an overview of the entire course. This video provides an overview of the entire course. Understanding Customer Segmentation 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.
  • Understand clustering techniques
  • Perform clustering techniques
Understanding Customer Segmentation 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.
  • Understand clustering techniques
  • Perform clustering techniques
Understanding Customer Segmentation 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.
  • Understand clustering techniques
  • Perform clustering techniques
Understanding Customer Segmentation 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.
  • Understand clustering techniques
  • Perform clustering techniques
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.
  • Understand clustering techniques
  • Perform clustering techniques
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.
  • Understand clustering techniques
  • Perform clustering techniques
Clustering Methods – K means and Hierarchical There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
Clustering Methods – K means and Hierarchical There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
Clustering Methods – K means and Hierarchical There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
Clustering Methods – K means and Hierarchical There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
  • Follow the steps for K-means clustering
  • Perform the steps for hierarchical clustering
  • Compare and analyze the output
Clustering Methods – Model Based, Other and Comparison In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
Clustering Methods – Model Based, Other and Comparison In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
Clustering Methods – Model Based, Other and Comparison In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
Clustering Methods – Model Based, Other and Comparison In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
  • Perform model-based clustering
  • Perform SOM
  • Compare the clustering methods
Building a Retail Recommendation Engine 3 lectures 14:41 What Is Recommendation? 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.
  • Understand recommendation
  • Know its types and techniques
Application of Methods and Limitations of Collaborative Filtering 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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
Practical Project As we are armed with the theory of recommendation, we will now build a recommendation engine.
  • Load the library and datasets
  • Create a recommendation engine using different approaches
  • Compare the results of the engines made using different methods
Building a Retail Recommendation Engine. 3 lectures 14:41 What Is Recommendation? 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.
  • Understand recommendation
  • Know its types and techniques
Application of Methods and Limitations of Collaborative Filtering 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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
Practical Project As we are armed with the theory of recommendation, we will now build a recommendation engine.
  • Load the library and datasets
  • Create a recommendation engine using different approaches
  • Compare the results of the engines made using different methods
What Is Recommendation? 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.
  • Understand recommendation
  • Know its types and techniques
What Is Recommendation? 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.
  • Understand recommendation
  • Know its types and techniques
What Is Recommendation? 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.
  • Understand recommendation
  • Know its types and techniques
What Is Recommendation? 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.
  • Understand recommendation
  • Know its types and techniques
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.
  • Understand recommendation
  • Know its types and techniques
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.
  • Understand recommendation
  • Know its types and techniques
Application of Methods and Limitations of Collaborative Filtering 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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
Application of Methods and Limitations of Collaborative Filtering 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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
Application of Methods and Limitations of Collaborative Filtering 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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
Application of Methods and Limitations of Collaborative Filtering 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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
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.
  • Compare data of user-based, item-based and content-based collaborative filtering
  • Understand the limitations of user based collaborative filtering
Practical Project As we are armed with the theory of recommendation, we will now build a recommendation engine Applying Neural Network to Healthcare Data 7 lectures...

Additional information

They should have prior knowledge of basic statistics and some experience with the basic data mining techniques and algorithms

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Advanced Data Mining projects with R

£ 150 VAT inc.

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