你好! Shipping to Taiwan with premium packaging for just NT$300 

Ship to
Taiwan
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Machine Learning: Models and Applications
Type
Physical Book
Publisher
Author
Language
English
Pages
160
Format
Hardcover
Dimensions
23.4x15.6x1.1 cm
ISBN13
9789360847999

Machine Learning: Models and Applications

Arpita Munsi, Sidhartha Patnaik (Author) · Wordpen Academics · Hardcover

Machine Learning: Models and Applications - Arpita Munsi, Sidhartha Patnaik

New Book Imported to Taiwan
Delivery: 24 Sep - 02 Oct Shipping: 4 to 5 business days.
NT$ 1,908
NT$ 1,908

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Machine Learning: Models and Applications"

Introduction to Machine Learning (ML): Provide a foundational understanding of ML as a subset of artificial intelligence focused on enabling systems to learn from data and improve performance without explicit programming. Types of Learning Models: Explore the three main types of ML models-supervised, unsupervised, and reinforcement learning-along with their use cases and differences in training methods. Common Algorithms: Discuss popular ML algorithms such as linear regression, decision trees, support vector machines, k-means clustering, and neural networks, and their strengths in different scenarios. Model Training and Evaluation: Explain the process of training ML models using datasets, and evaluating them with metrics like accuracy, precision, recall, and F1 score to ensure reliability and generalization. Applications Across Industries: Highlight practical applications of ML in fields such as healthcare (diagnosis prediction), finance (fraud detection), agriculture (yield forecasting), and e-commerce (recommendation systems). Data Preparation and Feature Engineering: Emphasize the importance of data cleaning, normalization, and feature selection in building effective ML models. Tools and Frameworks: Introduce key ML tools and libraries like Python, TensorFlow, Scikit-learn, and PyTorch that aid in model development and deployment. Challenges and Ethical Considerations: Address issues such as data bias, overfitting, interpretability, and the ethical implications of deploying ML systems in real-world environments.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Hardcover.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews