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Mastering Classification Algorithms for Machine Learning: Learn how to apply Classification algorithms for effective Machine Learning solutions by , Partha Majumdar is a nonfiction available to read on EtoBox.
What is Mastering Classification Algorithms for Machine Learning: Learn how to apply Classification algorithms for effective Machine Learning solutions about?
A practical guide to mastering Classification algorithms for Machine learning Key Features ● Get familiar with all the state-of-the-art classification algorithms for machine learning. ● Understand the mathematical foundations behind building machine learning models. ● Learn how to apply machine learning models to solve real-world industry problems. Description Classification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you. The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Naïve Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Naïve Bayes algorithm for classification is also covered. Moving forward, the book centers its at
Who reads Mastering Classification Algorithms for Machine Learning: Learn how to apply Classification algorithms for effective Machine Learning solutions?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- , Partha Majumdar
- Publisher
- BPB Publications
- Published
- 2023
- Language
- EN
- ISBN
- 9789355518514
- Category
- nonfiction
- Subjects
- Computer Science, Stem
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