Title
Practical Machine Learning In R
Delivery time: 8-12 business days (International)
Guides Professionals And Students Through The Rapidly Growing Field Of Machine Learning With Handson Examples In The Popular R Programming Languagemachine Learninga Branch Of Artificial Intelligence (Ai) Which Enables Computers To Improve Their Results And Learn New Approaches Without Explicit Instructionsallows Organizations To Reveal Patterns In Their Data And Incorporate Predictive Analytics Into Their Decisionmaking Process. Practical Machine Learning In R Provides A Handson Approach To Solving Business Problems With Intelligent, Selflearning Computer Algorithms.Bestselling Author And Data Analytics Experts Fred Nwanganga And Mike Chapple Explain What Machine Learning Is, Demonstrate Its Organizational Benefits, And Provide Handson Examples Created In The R Programming Language. A Perfect Guide For Professional Selftaught Learners Or Students In An Introductory Machine Learning Course, This Readerfriendly Book Illustrates The Numerous Realworld Business Uses Of Machine Learning Approaches. Clear And Detailed Chapters Cover Data Wrangling, R Programming With The Popular Rstudio Tool, Classification And Regression Techniques, Performance Evaluation, And More. Explores Data Management Techniques, Including Data Collection, Exploration And Dimensionality Reduction Covers Unsupervised Learning, Where Readers Identify And Summarize Patterns Using Approaches Such As Apriori, Eclat And Clustering Describes The Principles Behind The Nearest Neighbor, Decision Tree And Naive Bayes Classification Techniques Explains How To Evaluate And Choose The Right Model, As Well As How To Improve Model Performance Using Ensemble Methods Such As Random Forest And Xgboostpractical Machine Learning In R Is A Musthave Guide For Business Analysts, Data Scientists, And Other Professionals Interested In Leveraging The Power Of Ai To Solve Business Problems, As Well As Students And Independent Learners Seeking To Enter The Field.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Practical Machine Learning in R'? A: 'Practical Machine Learning in R' focuses on teaching professionals and students the principles of machine learning using the R programming language through hands-on examples.
- Q: Who are the authors of this book? A: The book is authored by Fred Nwanganga and Mike Chapple, both of whom are experts in data analytics.
- Q: What topics are covered in the book? A: The book covers topics including data wrangling, classification and regression techniques, performance evaluation, data management, unsupervised learning, and various machine learning algorithms.
- Q: Is this book suitable for beginners? A: 'Practical Machine Learning in R' is designed for both beginners and professionals, making it ideal for self-taught learners and students in introductory machine learning courses.
- Q: What is the page count of the book? A: The book contains 464 pages, providing in-depth coverage of machine learning concepts.
- Q: What is the publication date of 'Practical Machine Learning in R'? A: The book was published on May 27, 2020.
- Q: What type of binding does the book have? A: This edition of 'Practical Machine Learning in R' is available in paperback binding.
- Q: Does the book include practical exercises? A: Yes, the book includes hands-on examples and exercises to help readers apply machine learning concepts using R.
- Q: What are some machine learning techniques explained in the book? A: The book explains techniques such as Nearest Neighbor, Decision Tree, Naive Bayes classification, as well as ensemble methods like Random Forest and XGBoost.
- Q: Is this book useful for data analysts? A: 'Practical Machine Learning in R' is a valuable resource for business analysts, data scientists, and anyone interested in leveraging AI for practical business applications.