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Mastering Machine Learning with R

Mastering Machine Learning with R - Second Edition

By : Cory Lesmeister, Doug Ortiz , Vikram Dhillon, Miroslav Kopecky
2.8 (4)
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Mastering Machine Learning with R

Mastering Machine Learning with R

2.8 (4)
By: Cory Lesmeister, Doug Ortiz , Vikram Dhillon, Miroslav Kopecky

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (17 chapters)
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Deployment

If everything is done according to the plan up to this point, it might just come down to flipping a switch and your model goes live. Assuming that this is not the case, here are the tasks for this step:

  1. Deploying the plan.
  2. Monitoring and maintaining the plan.
  3. Producing the final report.
  4. Reviewing the project.

After the deployment and monitoring/maintenance and underway, it is crucial for you and those who will walk in your steps to produce a well-written final report. This report should include a white paper and briefing slide. I have to say that I resisted the drive to put my findings in a white paper as I was an indentured servant to the military's passion for PowerPoint slides. However, slides can and will be used against you, cherry-picked or misrepresented by various parties for their benefit. Trust me, that just doesn't happen with a white paper as it becomes an extension of your findings and beliefs. Use PowerPoint to brief stakeholders, but use that the white paper as the document of record and as a preread, should your organization insist on one. It is my standard procedure to create this white paper in R using knitr and LaTex.

Now for the all-important process review, you may have your own proprietary way of conducting it; but here is what it should cover, whether you conduct it in a formal or informal way:

  • What was the plan?
  • What actually happened?
  • Why did it happen or not happen?
  • What should be sustained in future projects?
  • What should be improved upon in future projects?
  • Create an action plan to ensure sustainment and improvement happen

That concludes the review of the CRISP-DM process, which provides a comprehensive and flexible framework to guarantee the success of your project and make you an agent of change.

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Mastering Machine Learning with R
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