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Title Page
Copyright and Credits
R Machine Learning Projects
About Packt
Why subscribe?
Packt.com
Dedication
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Exploring the Machine Learning Landscape
ML versus software engineering
Types of ML methods
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Transfer learning
ML terminology – a quick review
Deep learning
Big data
Natural language processing
Computer vision
Cost function
Model accuracy
Confusion matrix
Predictor variables
Response variable
Dimensionality reduction
Class imbalance problem
Model bias and variance
Underfitting and overfitting
Data preprocessing
Holdout sample
Hyperparameter tuning
Performance metrics
Feature engineering
Model interpretability
ML project pipeline
Business understanding
Understanding and sourcing the data
Preparing the data
Model building and evaluation
Model deployment
Learning paradigm
Datasets
Summary
Predicting Employee Attrition Using Ensemble Models
Philosophy behind ensembling
Getting started
Understanding the attrition problem and the dataset
K-nearest neighbors model for benchmarking the performance
Bagging
Bagged classification and regression trees (treeBag) implementation
Support vector machine bagging (SVMBag) implementation
Naive Bayes (nbBag) bagging implementation
Randomization with random forests
Implementing an attrition prediction model with random forests
Boosting
The GBM implementation
Building attrition prediction model with XGBoost
Stacking
Building attrition prediction model with stacking
Summary
Implementing a Jokes Recommendation Engine
Fundamental aspects of recommendation engines
Recommendation engine categories
Content-based filtering
Collaborative filtering
Hybrid filtering
Getting started
Understanding the Jokes recommendation problem and the dataset
Converting the DataFrame
Dividing the DataFrame
Building a recommendation system with an item-based collaborative filtering technique
Building a recommendation system with a user-based collaborative filtering technique
Building a recommendation system based on an association-rule mining technique
The Apriori algorithm
Content-based recommendation engine
Differentiating between ITCF and content-based recommendations
Building a hybrid recommendation system for Jokes recommendations
Summary
References
Sentiment Analysis of Amazon Reviews with NLP
The sentiment analysis problem
Getting started
Understanding the Amazon reviews dataset
Building a text sentiment classifier with the BoW approach
Pros and cons of the BoW approach
Understanding word embedding
Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus
Building a text sentiment classifier with GloVe word embedding
Building a text sentiment classifier with fastText
Summary
Customer Segmentation Using Wholesale Data
Understanding customer segmentation
Understanding the wholesale customer dataset and the segmentation problem
Categories of clustering algorithms
Identifying the customer segments in wholesale customer data using k-means clustering
Working mechanics of the k-means algorithm
Identifying the customer segments in the wholesale customer data using DIANA
Identifying the customer segments in the wholesale customers data using AGNES
Summary
Image Recognition Using Deep Neural Networks
Technical requirements
Understanding computer vision
Achieving computer vision with deep learning
Convolutional Neural Networks
Layers of CNNs
Introduction to the MXNet framework
Understanding the MNIST dataset
Implementing a deep learning network for handwritten digit recognition
Implementing dropout to avoid overfitting
Implementing the LeNet architecture with the MXNet library
Implementing computer vision with pretrained models
Summary
Credit Card Fraud Detection Using Autoencoders
Machine learning in credit card fraud detection
Autoencoders explained
Types of AEs based on hidden layers
Types of AEs based on restrictions
Applications of AEs
The credit card fraud dataset
Building AEs with the H2O library in R
Autoencoder code implementation for credit card fraud detection
Summary
Automatic Prose Generation with Recurrent Neural Networks
Understanding language models
Exploring recurrent neural networks
Comparison of feedforward neural networks and RNNs
Backpropagation through time
Problems and solutions to gradients in RNN
Exploding gradients
Vanishing gradients
Building an automated prose generator with an RNN
Implementing the project
Summary
Winning the Casino Slot Machines with Reinforcement Learning
Understanding RL
Comparison of RL with other ML algorithms
Terminology of RL
The multi-arm bandit problem
Strategies for solving MABP
The epsilon-greedy algorithm
Boltzmann or softmax exploration
Decayed epsilon greedy
The upper confidence bound algorithm
Thompson sampling
Multi-arm bandit – real-world use cases
Solving the MABP with UCB and Thompson sampling algorithms
Summary
The Road Ahead
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