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- Think Bayes: Bayesian Statistics in Python
Think Bayes: Bayesian Statistics in Python
CZK 1161
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With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation.
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| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to deepen their understanding of Bayesian statistics and implement models in Python.
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Statistics Students
Great resource for students studying statistics, offering practical examples and hands-on coding exercises in Python.
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Machine Learning Enthusiasts
Beneficial for ML practitioners interested in incorporating Bayesian methods into their algorithms for better predictive modeling.
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Beginners in Stats
Not suitable for beginners without a foundational understanding of statistics, as it dives deeply into complex concepts.
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Non-Technical Users
Individuals without a programming background may struggle with the Python coding examples included throughout the book.
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Advanced Statisticians
Experienced statisticians may find the material too basic and not comprehensive enough for advanced applications or research.
POPIS PRODUKTU
Think Bayes: Bayesian Statistics in Python
Otázky a odpovědi zákazníků
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otázka:
Do I need advanced mathematics to understand this book?
Odpovědět: No, a basic understanding of probability is sufficient. -
otázka:
What programming skills do I need before starting this book?
Odpovědět: Familiarity with Python programming is required. -
otázka:
Can I apply what I learn to real-world problems?
Odpovědět: Yes, the book emphasizes practical applications of Bayesian statistics.
Probability & Statistics Editorial Review
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CZK 1161
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Služby a výhody
- Designed for Python programmers with a basic understanding of probability.
- Focuses on solving statistical problems through code rather than complex math.
- Covers Bayesian fundamentals clearly through practical examples.
- Teaches computational methods for real-world applications.
- Includes engaging examples like Dungeons & Dragons and sports.
- Ideal for beginners looking to dive into Bayesian statistics.
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