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- Machine Learning Algorithms: Handbook
Machine Learning Algorithms: Handbook
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Packed with practical examples using Python and code snippets, you'll gain a hands-on understanding of how each algorithm works and learn to implement them in real projects.
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Detalji o proizvodu
- In Machine Learning Algorithms: Handbook, Aman Kharwal, founder of Statso.io, takes you on an enlightening journey through the fascinating world of machine learning. Whether you are a seasoned data scientist or a curious beginner, this book provides a holistic overview of the essential algorithms that form the backbone of modern machine learning.With clarity and precision, Aman demystifies complex concepts, guiding you step-by-step through the fundamentals of regression, classification, clustering, deep learning, and time series forecasting. Each chapter presents a deep dive into a specific algorithm, equipping you with the knowledge and skills to tackle real-world problems head-on.Key Features: 1. Clear Explanations of Machine Learning Algorithms: The book offers clear and concise explanations of machine learning algorithms, ensuring that readers of all levels can grasp the concepts effortlessly.2. Hands-On Approach: Packed with practical examples using Python and code snippets, you'll gain a hands-on understanding of how each algorithm works and learn to implement them in real projects.3. Comprehensive Coverage: From linear regression and support vector machines to decision trees and neural networks, the book covers a wide array of algorithms, giving you a solid foundation to explore diverse problem domains.4. Performance Evaluation Methods: Learn how to evaluate the effectiveness of your models, identify areas for improvement, and optimize their performance using industry-standard evaluation techniques.5. Data Preprocessing Techniques: Discover the critical elements of data preprocessing that lay the groundwork for building robust and accurate machine learning models.6. Time Series Forecasting: Explore advanced algorithms specifically designed for time series data, a critical component of numerous real-world applications.7. Appendix for Easy Reference: Access all parameters of commonly used machine learning algorithms in a handy appendix, facilitating efficient model tuning.Whether you are interested in learning the fundamentals of all Machine Learning algorithms, implementation of Machine Learning algorithms using Python, or preparing for an interview, Machine Learning Algorithms: Handbook will help you in every way.
| Publisher | Clever Fox Publishing |
| Publication date | September 9, 2023 |
| Language | English |
| Print length | 234 pages |
| ISBN-10 | 935648483X |
| ISBN-13 | 978-9356484832 |
| Dimensions | 6 x 0.61 x 9 inches (15.2 x 1.5 x 22.9 cm) |
| Package Weight | 1 Pound |
Who Should Buy?
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Beginners in ML
Ideal for those new to machine learning, providing foundational knowledge and easy-to-understand algorithms.
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Data Science Students
Perfect for students pursuing data science, offering practical examples and a solid understanding of various algorithms.
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Self-Learners
Suitable for individuals interested in self-learning machine learning concepts and applications through a structured handbook.
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Advanced ML Experts
Not suitable for seasoned machine learning professionals seeking in-depth, cutting-edge research or advanced topics.
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Intelligence & Semantics Editorial Review
The "Machine Learning Algorithms: Handbook" is receiving positive feedback from customers. Readers appreciate the clear and easy-to-understand explanations of machine learning algorithms, along with the accompanying code implementations. The inclusion of bonus chapters on neural networks and a comprehensive explanation of important hyperparameters for all algorithms is also well-received. Reviewers note that the book is great for beginners, providing a simple and concise handbook for those looking to gain a clear understanding of machine learning concepts. Despite the higher price, readers feel that the usage and print quality justify the cost. Additionally, followers of the author's projects find the book to be exceptional and are looking forward to more similar publications in the future.
Customer Reviews & Ratings
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5 zvijezda
55%
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4 zvijezda
29%
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3 zvijezda
8%
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2 zvijezda
4%
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1 zvijezda
4%
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Pros
- Clear and easy-to-understand explanations
- In-depth code implementations
- Bonus chapters on neural networks
- Comprehensive explanation of important hyperparameters
- Great for beginners
Cons
- Higher price than other similar books
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Karakteristike i prednosti
- Clear and concise explanations of machine learning algorithms
- Practical examples using Python and code snippets
- Covers a wide array of algorithms
- Teaches how to evaluate and optimize model performance
- Critical elements for building robust machine learning models
- Explores advanced algorithms for time series data
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