Machine Learning Using R : With Time Series and Industry-Based Use Cases in R / / by Karthik Ramasubramanian, Abhishek Singh

• Record Nr.: UNINA9910338013903321
• Autore: Ramasubramanian Karthik
• Pubbl/distr/stampa: Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019
• ISBN: 9781523150403; 1523150408; 9781484242155; 1484242157
• Edizione: [2nd ed. 2019.]
• Descrizione fisica: 1 online resource (712 pages)
• Disciplina: 006.31
• Soggetti: Artificial intelligence; Open source software; Computer programming; Programming languages (Electronic computers); R (Computer program language); Artificial Intelligence; Open Source; Programming Languages, Compilers, Interpreters
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Chapter 1: Introduction to Machine Learning -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Visualization of Data -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Models: Theory and Practice -- Chapter 7: Machine Learning Model Evaluation -- Chapter 8: Model Performance Improvement -- Chapter 9: Time Series Modelling -- Chapter 10: Scalable Machine Learning and related technology -- Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlow.
• Sommario/riassunto: Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it ; to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. You will: Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R.