Advanced Machine Learning with R: Tackle data analytics and machine learning challenges and build complex applications with R 3.5
Authors: Cory Lesmeister – Dr. Sunil Kumar Chinnamgari
ISBN-10: 1838641777
ISBN-13: 9781838641771
Publication Date 出版日期: 2019-05-20
Print Length 页数: 664 pages
Book Description
By finelybook
Master machine learning techniques with real-world projects that interface TensorFlow with R,H2O,MXNet,and other languages
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 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
contents
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 Jokes 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
