Lyt når som helst, hvor som helst

Nyd den ubegrænsede adgang til tusindvis af spændende e- og lydbøger - helt gratis

  • Lyt og læs så meget du har lyst til
  • Opdag et kæmpe bibliotek fyldt med fortællinger
  • Eksklusive titler + Mofibo Originals
  • Opsig når som helst
Start tilbuddet
DK - Details page - Device banner - 894x1036

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

Sprog
Engelsk
Format
Kategori

Fakta

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more

Key Features

• Master machine learning, deep learning, and predictive modeling concepts in R 3.5 • Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains • Implement smart cognitive models with helpful tips and best practices

Book Description

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you'll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You'll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.

What you will learn

• Explore deep neural networks and various frameworks that can be used in R • Develop a joke recommendation engine to recommend jokes that match users' tastes • Create powerful ML models with ensembles to predict employee attrition • Build autoencoders for credit card fraud detection • Work with image recognition and convolutional neural networks • Make predictions for casino slot machine using reinforcement learning • Implement NLP techniques for sentiment analysis and customer segmentation

Who this book is for

If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.

© 2019 Packt Publishing (E-bog): 9781789806090

Release date

E-bog: 14. januar 2019

Andre kan også lide...

  1. Machine Learning: Deep Learning, Text Analytics, and Reinforcement Learning with Big Data David Feldspar
  2. The Mathematics of Various Entertaining Subjects: Research in Recreational Math Jason Rosenhouse
  3. Elliptic Tales: Curves, Counting, and Number Theory Avner Ash
  4. A Biologist's Guide to Mathematical Modeling in Ecology and Evolution Troy Day
  5. Mathematics in Nature: Modeling Patterns in the Natural World John Adam
  6. Discrete and Computational Geometry Satyan L. Devadoss
  7. Heavenly Mathematics: The Forgotten Art of Spherical Trigonometry Glen Van Brummelen
  8. The Creativity Code: How AI is learning to write, paint and think Marcus du Sautoy
  9. Deep Learning: Machine Learning and Data Analytics Explained David Feldspar
  10. Artificial Intelligence For Dummies Luca Massaron
  11. The Princeton Companion to Mathematics Timothy Gowers
  12. Everyday Calculus: Discovering the Hidden Math All around Us Oscar E. Fernandez
  13. Neural Networks for Beginners: A Journey Through the Brain of AI Steve Abrams
  14. Deep Learning: Guide to Machine Learning and Artificial Intelligence David Feldspar
  15. Mathematics: A Very Short Introduction Timothy Gowers
  16. Machine Learning: The New AI Ethem Alpaydi
  17. Selfsimilar Processes Paul Embrechts
  18. Individual-based Modeling and Ecology Steven F. Railsback
  19. Enhancing Deep Learning Performance Using Displaced Rectifier Linear Unit David Macêdo
  20. Calculating the Cosmos: How Mathematics Unveils the Universe Ian Stewart
  21. Artificial Intelligence in the Modern World: Transformative Technologies and Ethical Implications: Navigating the Impact of AI on Society, Economy, and Culture David Chang
  22. X and the City: Modeling Aspects of Urban Life John Adam
  23. Deep Learning John D. Kelleher
  24. Data Science and Machine Learning Demystified: Mastering Data Science and Machine Learning: Advanced Techniques and Applications Liam Stone
  25. Dynamic Models in Biology John Guckenheimer
  26. The Self-Assembling Brain: How Neural Networks Grow Smarter Peter Robin Hiesinger
  27. Artificial Intelligence Class 5 Geeta Zunjani
  28. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World Karim R. Lakhani
  29. Mathematical Modeling of Earth's Dynamical Systems: A Primer Rudy Slingerland
  30. Building, Training and Hardware for LLM AI: A Comprehensive Guide to Large Language Model Development, Training, and Hardware Infrastructure Et Tu Code
  31. Bear And Vector Calculus Dr. R. Prabakaran
  32. The Best Writing on Mathematics 2010 Mircea Pitici
  33. A Survey of Computational Physics: Introductory Computational Science José Páez
  34. Numerical Analysis Larkin Ridgway Scott
  35. The Deep Learning Revolution Terrence J. Sejnowski
  36. Demystifying LLM, AI Mathematics, and Hardware Infra: A comprehensive guide to understanding Large Language Models, AI Mathematics, and its Hardware Infrastructure Et Tu Code
  37. Unsolved Problems in Mathematical Systems and Control Theory Vincent D. Blondel
  38. Real Analysis with Economic Applications Efe A. Ok
  39. AI for beginners: Begin your AI developer journey in 2024 Et Tu Code
  40. Fundamentals of Machine Learning: A no code no math book on understanding fundamentals of modern ML & AI DSA Shots
  41. Machine Learning for Beginners: An Introduction to Artificial Intelligence and Machine Learning John Slavio
  42. Artificial Intelligence with Python for Beginners: Comprehensive Guide to Building AI Applications James Ferry
  43. Mathematical Techniques in Finance: Tools for Incomplete Markets - Second Edition Ales Cerný
  44. Artificial General Intelligence: (The MIT Press Essential Knowledge series) Julian Togelius
  45. Viewpoints: Mathematical Perspective and Fractal Geometry in Art Marc Frantz
  46. Mathematical Knowledge and the Interplay of Practices José Ferreirós
  47. Mathematics for the Life Sciences Suzanne Lenhart

Vælg dit abonnement

  • Over 600.000 titler

  • Download og nyd titler offline

  • Eksklusive titler + Mofibo Originals

  • Børnevenligt miljø (Kids Mode)

  • Det er nemt at opsige når som helst

Flex

For dig som vil prøve Mofibo.

89 kr. /måned
  • 1 konto

  • 20 timer/måned

  • Gem op til 100 ubrugte timer

  • Eksklusivt indhold hver uge

  • Fri lytning til podcasts

  • Ingen binding

Prøv gratis
Den mest populære

Premium

For dig som lytter og læser ofte.

129 kr. /måned
  • 1 konto

  • 100 timer/måned

  • Eksklusivt indhold hver uge

  • Fri lytning til podcasts

  • Ingen binding

Start tilbuddet

Unlimited

For dig som lytter og læser ubegrænset.

149 kr. /måned
  • 1 konto

  • Ubegrænset adgang

  • Eksklusivt indhold hver uge

  • Fri lytning til podcasts

  • Ingen binding

Start tilbuddet

Family

For dig som ønsker at dele historier med familien.

Fra 179 kr. /måned
  • 2-6 konti

  • 100 timer/måned pr. konto

  • Fri lytning til podcasts

  • Kun 39 kr. pr. ekstra konto

  • Ingen binding

2 konti

179 kr. /måned
Prøv gratis