Machine Learning with R Quick Start Guide: A beginner’s guide to implementing machine learning techniques from scratch using R 3.5

Machine Learning with R Quick Start Guide: A beginner’s guide to implementing machine learning techniques from scratch using R 3.5
Machine Learning with R Quick Start Guide: A beginner’s guide to implementing machine learning techniques from scratch using R 3.5 by Ivan Pastor Sanz
English | 2019 | ISBN: 1838644338 | 250 Pages | EPUB | 10 MB

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.
Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline.
From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling.
By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.
What you will learn

  • Introduce yourself to the basics of machine learning with R 3.5
  • Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results
  • Learn to build predictive models with the help of various machine learning techniques
  • Use R to visualize data spread across multiple dimensions and extract useful features
  • Use interactive data analysis with R to get insights into data
  • Implement supervised and unsupervised learning, and NLP using R libraries