Advanced Data Mining projects with R

This fast-paced video tutorial will help you solve predictive modeling problems using the most popular data mining algorithms through simple, practical use cases. This is your step-by-step guide to developing complex data mining projects.

  • Comprehensive training through 17 video sessions.
  • Apply dimensionality reduction and neural network techniques in real-world projects.
  • Practical projects on real-world use-cases presented in a very easy to understand manner.
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    Self-Paced

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    About Advanced Data Mining projects with R

    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.

    Course Objectives
    • 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
    Curriculum
    Module 1:

    Clustering with E-Commerce Data

    • The Course Overview
    • Understanding Customer Segmentation
    • Clustering Methods – K means and Hierarchical
    • Clustering Methods – Model Based, Other and Comparison
    Module 2:

    Building a Retail Recommendation Engine

    • What Is Recommendation?
    • Application of Methods and Limitations of Collaborative Filtering
    • Practical Project
    Module 3:

    Dimensionality Reduction

    • Why Dimensionality Reduction?
    • Practical Project around Dimensionality Reduction
    • Parametric Approach to Dimension Reduction
    Module 4:

    Applying Neural Network to Healthcare Data

    • Introduction to Neural Networks
    • Understanding the Math Behind the Neural Network
    Module 5:

    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
    Instructor

    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.

    Certification

    A test will be conducted at the end of the course. On completion of the test with a minimum of 70% marks, training.com will issue a certificate of successful completion from NIIT.

    Five re-attempts will be provided in case the candidate scores less than 70%.

    A Participation certificate will be issued if the candidate does not score 70% after five attempts.

    Pre-requisites

    Basic knowledge of data analysitcs and basic programming background with Math as one of the subjects.

    FAQs

    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 support@training.com , 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?

      Minimum system requirements for accessing the courses are:

    • Personal computer or Laptop with web camera
    • Headphone with Mic
    • Minimum 4 Mbps broadband connection
    • A self-diagnostic test to meet necessary requirements to be done is available at

      https://na1cps.adobeconnect.com/common/help/en/support/meeting_test.htm

      Please note that webcam, mike and internet speed cannot be verified through this link.

    Course Features

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