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- Machine Learning
Machine Learning
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ILS 196
تفاصيل السعر
باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )
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Unlock the power of machine learning with a guide designed to take you from foundational concepts to cutting-edge applications.
شحن
سريع
استرجاع
مجاني*
تغليف آمن
منتجات أصلية %100
الامتثال لمعيار PCI DSS
حاصل على شهادة ISO 27001
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تفاصيل المنتج
| Publisher | Independently published |
| Publication date | April 3, 2025 |
| Language | English |
| Print length | 584 pages |
| ISBN-13 | 979-8316633296 |
| Item Weight | 3.61 pounds (1.64 kg) |
| Dimensions | 8.5 x 1.32 x 11 inches (21.6 x 3.4 x 27.9 cm) |
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وصف المنتج
Machine Learning
أسئلة العملاء & الإجابات
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سؤال:
What is Machine Learning?
إجابه: Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms that allow computers to learn from and make predictions based on data. It enables systems to improve their performance over time without being explicitly programmed. For instance, a machine learning model can analyze vast amounts of data to identify patterns and make decisions, making it essential in areas like predictive analytics, natural language processing, and image recognition. -
سؤال:
How does Machine Learning work?
إجابه: Machine Learning operates on the principle of feeding vast datasets into algorithms, which then identify patterns and make predictions. The process typically involves training a model using a dataset, where it learns the relationships between input features and output labels. After training, the model can be tested on new data to see how accurately it can predict outcomes. This functionality is crucial across various fields, including finance for credit scoring, healthcare for diagnosis, and e-commerce for personalized recommendations. -
سؤال:
What are the types of Machine Learning?
إجابه: The main types of Machine Learning are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the model is trained on labeled data, allowing it to learn the mapping between inputs and outputs. Unsupervised learning involves finding hidden patterns in data without pre-existing labels, often used for clustering and association tasks. Reinforcement learning teaches models to make a sequence of decisions by rewarding desirable outcomes, commonly applied in robotics and gaming. -
سؤال:
What are some practical applications of Machine Learning?
إجابه: Machine Learning has a wide array of applications, including fraud detection in banking, speech recognition in virtual assistants, and recommendation engines in e-commerce that personalize the shopping experience. For example, Netflix uses machine learning algorithms to analyze viewers' behavior and suggest movies and shows that they might enjoy, greatly enhancing user satisfaction. -
سؤال:
What is the role of data in Machine Learning?
إجابه: Data is the cornerstone of Machine Learning as it provides the necessary information for algorithms to learn and make predictions. High-quality data allows for more accurate models. This includes structured data, such as databases, and unstructured data, such as text and images. For instance, in real estate, data about past property sales can help machine learning models predict future prices. -
سؤال:
Can Machine Learning models interpret unstructured data?
إجابه: Yes, Machine Learning models are increasingly capable of interpreting unstructured data, such as text, images, and videos. Techniques like natural language processing enable models to understand language nuances, while computer vision allows machines to interpret visual data. This capability paves the way for applications like sentiment analysis on social media and automated image tagging, adding significant value to businesses. -
سؤال:
What is overfitting in Machine Learning?
إجابه: Overfitting occurs when a machine learning model learns the details and noise in the training data to the extent that it negatively impacts its performance on new data. This happens when the model is overly complex compared to the amount of data available. To avoid overfitting, techniques such as cross-validation, pruning, and regularization are commonly employed. For example, simplifying a model might improve its performance on unseen data. -
سؤال:
How does Machine Learning differ from traditional programming?
إجابه: Unlike traditional programming, where developers write explicit instructions for the computer to follow, Machine Learning enables computers to learn from data patterns and make adjustments independently. This adaptive approach is particularly useful in situations where it is challenging to define rules explicitly, such as recognizing speech or images. Over time, this leads to systems that continuously improve as they are exposed to more data. -
سؤال:
What skills are needed to work in Machine Learning?
إجابه: To work in Machine Learning, one should possess a strong foundation in mathematics, particularly statistics, and linear algebra, along with proficiency in programming languages like Python or R. Familiarity with machine learning frameworks such as TensorFlow and PyTorch is also advantageous. Professionals often apply these skills in diverse fields, such as developing automated trading systems in finance or creating personalized healthcare solutions. -
سؤال:
Where can I buy Machine Learning in Palestine?
إجابه: You can purchase Machine Learning products from Ubuy, a trusted online platform that offers a variety of machine learning resources and tools tailored to your needs in Palestine. Ubuy provides a comprehensive selection of books, software, and online courses that can enhance your understanding and practical application of machine learning concepts.
AI & Machine Learning Editorial Review
Machine Learning: Algorithms, Theory and Practice is an exceptional resource that bridges the gap between theory and hands-on practice, making it perfect for both newcomers and experienced readers. Its structured approach covers everything from basic regression to advanced topics like transformers and generative AI, while the inclusion of over 300 glossary terms and clear guidance using libraries like NumPy and scikit-learn ensures a great learning experience. Readers appreciate the clarity and digestibility of the explanations, which help demystify complex subjects without being overwhelming. This book offers a practical way to master machine learning without feeling lost in jargon.
مراجعات العملاء وتقييماتهم
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إيجابيات
- Structured approach from basics to advanced topics
- Over 300 glossary terms for quick reference
- Clear guidance using popular libraries
- Real-world coding tutorials for practical learning
- Balanced explanations of concepts and code
سلبيات
- Some chapters delve into advanced mathematics, which may require extra study.
منصة موثوقة وثقة كاملة للمشتري
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تاريخ سعر المنتج
معلومات مهمة
- القيود: بالنسبة للمنتجات التي يتم شحنها دولياً، يُرجى ملاحظة أن أي ضمان من الشركة المصنعة قد لا يكون صالحاً؛ قد لا تتوفر خيارات خدمة الشركة المصنعة؛ قد لا تكون أدلة المنتج والتعليمات وتحذيرات السلامة مكتوبة بلغة بلد المقصد؛ قد لا يتم تصميم المنتجات (والمواد المصاحبة لها) وفقاً لمعايير بلد الوجهة والمواصفات ومتطلبات الملصقات؛ وقد لا تتوافق المنتجات مع الجهد الكهربي المستخدم في بلد الوجهة والمعايير الكهربائية الأخرى (تتطلب استخدام محوّل كهربي أو جهاز تحويل إذا كان ذلك مناسباً). المستلم مسؤول عن ضمان إمكانية استيراد المنتج بشكل قانوني إلى بلد الوجهة. عند الطلب من يوباي أو الشركات التابعة لها، يكون المستلم هو المستورد المسجل ويجب أن يلتزم بجميع القوانين واللوائح الخاصة ببلد الوجهة.
- ليست كل المنتجات المدرجة على يوباي معروضة للبيع، لأن يوباي هو محرك بحث عالمي. المنتجات تخضع للوائح التصدير / التجارة.
ILS 196
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09
هذا المنتج غير ممنوع في بلدي. (الرجاء الضغط على الرابط أعلاه إذا لم يكن هذا المنتج ممنوعاً في بلدك ، لذلك سيقوم فريقنا بمراجعته والسماح به.)
كمية:
نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.
المميزات والفوائد
- Comprehensive hands-on guide for mastering machine learning.
- Covers foundational AI and Python concepts progressively.
- Includes tutorials on data preprocessing and classical algorithms.
- Features advanced topics like deep learning, NLP, and generative AI.
- Offers real-world coding exercises and practical coding tutorials.
- Bonus content includes a glossary and guidance for using Jupyter Notebooks.
ضمان Ubuy
تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني*، وتغليف آمن لكل طلب.