R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Cover
Title Page
Copyright and Credits
About Packt
Dedication
Contributors
Table of Contents
Preface
Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: 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
Chapter 8: 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
Chapter 9: 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
Appendix: The Road Ahead
Other Books You May Enjoy
Index