Title
The Art Of Machine Learning: A Handson Guide To Machine Learning With R,Used
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Learn to expertly apply a range of machine learning methods to real data with this practical guide.Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math.As you work through the book, youll learn how to implement a range of powerful ML techniques, starting with the kNearest Neighbors (kNN) method and random forests, and moving on to gradient boosting, support vector machines (SVMs), neural networks, and more.With the aid of real datasets, youll delve into regression models through the use of a bikesharing dataset, explore decision trees by leveraging New York City taxi data, and dissect parametric methods with baseball player stats. Youll also find expert tips for avoiding common problems, like handling dirty or unbalanced data, and how to troubleshoot pitfalls.Youll also explore:How to deal with large datasets and techniques for dimension reduction Details on how the BiasVariance Tradeoff plays out in specific ML methods Models based on linear relationships, including ridge and LASSO regression Realworld image and text classification and how to handle time series dataMachine learning is an art that requires careful tuning and tweaking. With The Art of Machine Learning as your guide, youll master the underlying principles of ML that will empower you to effectively use these models, rather than simply provide a few stock actions with limited practical use.Requirements: A basic understanding of graphs and charts and familiarity with the R programming language
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has two hundred seventy-two pages. It provides comprehensive insights into machine learning techniques and their practical applications.
- Q: What is the binding type of this book? A: The binding type is paperback. This makes it lightweight and easy to handle for readers.
- Q: What are the dimensions of the book? A: The book measures seven point zero one inches in length, zero point six four inches in width, and nine point two five inches in height. These dimensions make it portable for reading.
- Q: What programming language is required for this book? A: A basic understanding of the R programming language is required. This book focuses on applying machine learning techniques using R.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It explains machine learning concepts without requiring advanced math knowledge.
- Q: What topics are covered in this book? A: The book covers topics like k-Nearest Neighbors, random forests, gradient boosting, and support vector machines. It also touches on regression models and time series data.
- Q: How should I store this book? A: Store this book in a cool, dry place away from direct sunlight. This will help preserve its condition over time.
- Q: Can this book be used for self-study? A: Yes, this book is ideal for self-study. It includes practical examples and real datasets for hands-on learning.
- Q: Is this book safe for children? A: Yes, this book is safe for older children. However, it is recommended for those with a basic understanding of graphs and charts.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, contact the retailer for return instructions. Most retailers have policies for damaged items.
- Q: Does this book provide troubleshooting tips? A: Yes, the book includes expert tips for avoiding common problems in machine learning. It addresses issues like handling unbalanced data.
- Q: Is there a warranty on this book? A: Books typically do not come with a warranty. However, check the retailer's return policy for any guarantees.
- Q: Does this book include exercises or projects? A: Yes, the book includes practical examples that serve as exercises for applying machine learning techniques.
- Q: Can I use this book for a course? A: Yes, this book can be used for a course on machine learning. It is structured to facilitate learning in an academic setting.
- Q: What if I have more questions about this book? A: If you have more questions, consider reaching out to the publisher or checking online reviews for additional information.