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- Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5
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Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5
Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Ebook
R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.
This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood.
By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood.
By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
- 1. Preparing and Understanding Data
- 2. Linear Regression
- 3. Logistic Regression
- 4. Advanced Feature Selection in Linear Models
- 5. K-Nearest Neighbors and Support Vector Machines
- 6. Tree-Based Classification
- 7. Neural Networks and Deep Learning
- 8. Creating Ensembles and Multiclass Methods
- 9. Cluster Analysis
- 10. Principal Component Analysis
- 11. Association Analysis
- 12. Time Series and Causality
- 13. Text Mining
- 14. Exploring the Machine Learning Landscape
- 15. Predicting Employee Attrition Using Ensemble Models
- 16. Implementing a Joke Recommendation Engine
- 17. Sentiment Analysis of Amazon Reviews with NLP
- 18. Customer Segmentation Using Wholesale Data
- 19. Image Recognition Using Deep Neural Networks
- 20. Credit Card Fraud Detection Using Autoencoders
- 21. Automatic Prose Generation with Recurrent Neural Networks
- 22. Winning the Casino Slot Machines with Reinforcement Learning
- Title: Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5
- Author: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
- Original title: Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5
- ISBN: 9781838641771, 9781838641771
- Date of issue: 2019-05-20
- Format: Ebook
- Item ID: e_2aux
- Publisher: Packt Publishing