It is a complete book that investigates the basic ideas, valuable methods, and advanced uses of machine learning. Starting with basic concepts, the book goes over different types of machine learning, such as controlled, unsupervised, and reinforcement learning, including their methods and how they can be used in the real world. Readers are taken through the most important steps of preparing data, which include cleaning, transforming, and creating features. Along with more advanced topics like ensemble methods and deep learning, the book talks about a wide range of algorithms, including decision trees, neural networks, support vector machines, and linear regression.
It talks about evaluation metrics and model selection techniques to help people figure out how to measure success and pick the best method for each problem. The focus is on practical application, giving information on how to use machine learning models and talking about ethics issues like bias, privacy, and openness. The book is mostly about well-known libraries like Scikit-learn and TensorFlow. It shows how to use these tools to build intelligent systems in the real world. "The Fundamentals of Machine Learning" gives people who read it, like students, researchers, data scientists, and businesspeople, valuable tips and information on how to use data to their advantage.
The goal is to encourage new ideas, deal with challenging problems, and make choices based on facts. Machine learning and AI are changing quickly, so it's essential to keep up with new developments, social issues, and best practices. The field is full of endless options that encourage research, new ideas, and making a difference in the development of intelligent systems. People who read the book are encouraged to learn more about new ideas like automatic machine learning (AutoML), explainable AI (XAI), and how to combine quantum computing with machine learning.
It is a complete book that investigates the basic ideas, valuable methods, and advanced uses of machine learning. Starting with basic concepts, the book goes over different types of machine learning, such as controlled, unsupervised, and reinforcement learning, including their methods and how they can be used in the real world. Readers are taken through the most important steps of preparing data, which include cleaning, transforming, and creating features. Along with more advanced topics like ensemble methods and deep learning, the book talks about a wide range of algorithms, including decision trees, neural networks, support vector machines, and linear regression.
It talks about evaluation metrics and model selection techniques to help people figure out how to measure success and pick the best method for each problem. The focus is on practical application, giving information on how to use machine learning models and talking about ethics issues like bias, privacy, and openness. The book is mostly about well-known libraries like Scikit-learn and TensorFlow. It shows how to use these tools to build intelligent systems in the real world. "The Fundamentals of Machine Learning" gives people who read it, like students, researchers, data scientists, and businesspeople, valuable tips and information on how to use data to their advantage.
The goal is to encourage new ideas, deal with challenging problems, and make choices based on facts. Machine learning and AI are changing quickly, so it's essential to keep up with new developments, social issues, and best practices. The field is full of endless options that encourage research, new ideas, and making a difference in the development of intelligent systems. People who read the book are encouraged to learn more about new ideas like automatic machine learning (AutoML), explainable AI (XAI), and how to combine quantum computing with machine learning.
The Fundamentals of Machine Learning: Building Intelligent Systems from Data
82
The Fundamentals of Machine Learning: Building Intelligent Systems from Data
82
Product Details
| ISBN-13: | 9798330443512 |
|---|---|
| Publisher: | Ethan Bennett |
| Publication date: | 09/24/2024 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 82 |
| File size: | 3 MB |