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Index
Cover Front Matter 1. Introduction to Machine Learning and R 2. Data Preparation and Exploration 3. Sampling and Resampling Techniques 4. Data Visualization in R 5. Feature Engineering 6. Machine Learning Theory and Practice 7. Machine Learning Model Evaluation 8. Model Performance Improvement 9. Time Series Modeling 10. Scalable Machine Learning and Related Technologies 11. Deep Learning Using Keras and TensorFlow Back Matter
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