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

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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