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.
- 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
Clustering with E-Commerce Data
- The Course Overview
- Understanding Customer Segmentation
- Clustering Methods – K means and Hierarchical
- Clustering Methods – Model Based, Other and Comparison
Building a Retail Recommendation Engine
- What Is Recommendation?
- Application of Methods and Limitations of Collaborative Filtering
- Practical Project
- Why Dimensionality Reduction?
- Practical Project around Dimensionality Reduction
- Parametric Approach to Dimension Reduction
Applying Neural Network to Healthcare Data
- Introduction to Neural Networks
- Understanding the Math Behind the Neural Network
Applying Neural Network to Healthcare Data
- Neural Network Implementation in R
- Neural Networks for Prediction
- Neural Networks for Classification
- Neural Networks for Forecasting
- Merits and Demerits of Neural Netwo
Instructors are handpicked from a selected group of industry experts and mentors and trained to deliver the best online learning experience. All training.com instructors have at least ten years of industry experience and extensive functional expertise in the field they train.
NIIT Certification on completion of the program.
Basic knowledge of data analysitcs and basic programming background with Math as one of the subjects.
Who Should join this course ?
This course comes as an ideal choice for Data Science professionals involved in complex data analytics and data mining techniques. Professionals working on Data Mining Projects can also pursue this course to help them gain an extra edge over sophisticated data mining algorithms development using R Language.
Where can I find my session schedule?
The session schedule will be available in the training.com Student portal - Learning Plan section. You can login to your training.com account to view the same.
What is your refund policy?
Upon registering for the course, if for some reason you are unable or unwilling to participate in the course further, you can apply for a refund. You can initiate the refund any time before start of the second session of the course by sending an email to firstname.lastname@example.org , with your enrolment details and bank account details (where you want the amount to be transferred). Once you initiate a refund request, you will receive the amount within 21 days after confirmation and verification by our team. This is provided if you have not downloaded any courseware after registration.
Why is it called Self Paced course?
Self Paced courses are comprised of several learning videos into a course structure broken down into Learning Modules and Sessions. The learner is required to go through the videos topic-wise in the structure sequence of the course to learn the concepts. Being Self Paced, there is no intervention of any external faculty or additional mentor in learning.
Being a self paced course, how will my attendance be tracked and marked?
As you login into your training.com account to watch the videos, attendance for it will be marked automatically.
What are the minimum system requirements to attend the program?
- Personal computer or Laptop with web camera
- Headphone with Mic
- Minimum 4 Mbps broadband connection
Minimum system requirements for accessing the courses are:
A self-diagnostic test to meet necessary requirements to be done is available at
Please note that webcam, mike and internet speed cannot be verified through this link.