Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages
Key Features
Gain expertise in machine learning, deep learning and other techniques
Build intelligent end-to-end projects for finance, social media, and a variety of domains
Implement multi-class classification, regression, and clustering
Book DescriptionR 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 tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You'll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you 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 includes content from the following Packt products:
R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari
Mastering Machine Learning with R - Third Edition by Cory Lesmeister
What you will learn
Develop a joke recommendation engine to recommend jokes that match users' tastes
Build autoencoders for credit card fraud detection
Work with image recognition and convolutional neural networks
Make predictions for casino slot machine using reinforcement learning
Implement NLP techniques for sentiment analysis and customer segmentation
Produce simple and effective data visualizations for improved insights
Use NLP to extract insights for text
Implement tree-based classifiers including random forest and boosted tree
Who this book is forIf you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.
Key Features
Gain expertise in machine learning, deep learning and other techniques
Build intelligent end-to-end projects for finance, social media, and a variety of domains
Implement multi-class classification, regression, and clustering
Book DescriptionR 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 tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You'll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you 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 includes content from the following Packt products:
R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari
Mastering Machine Learning with R - Third Edition by Cory Lesmeister
What you will learn
Develop a joke recommendation engine to recommend jokes that match users' tastes
Build autoencoders for credit card fraud detection
Work with image recognition and convolutional neural networks
Make predictions for casino slot machine using reinforcement learning
Implement NLP techniques for sentiment analysis and customer segmentation
Produce simple and effective data visualizations for improved insights
Use NLP to extract insights for text
Implement tree-based classifiers including random forest and boosted tree
Who this book is forIf you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.
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