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Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition
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Data Science Projects with Python book takes you through the end-to-end process of exploring data and delivering machine learning models
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Детаљи о производу
- Gain hands-on experience of Python programming with industry-standard machine learning techniques
- Learn to think critically about data and form and test hypotheses
- Master the process of data exploration, processing, and visualization
- Implement and evaluate predictive machine learning models using scikit-learn
- Explore advanced concepts like XGBoost, SHAP values, algorithmic fairness, and model deployment
- Suitable for beginners with basic experience in Python programming and interest in statistics
| Publisher | Packt Publishing |
| Publication date | July 29, 2021 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 432 pages |
| ISBN-10 | 1800564481 |
| ISBN-13 | 978-1800564480 |
| Item Weight | 7.4 ounces (209.79 grams) |
| Dimensions | 7.5 x 0.98 x 9.25 inches (19.1 x 2.5 x 23.5 cm) |
OPIS PROIZVODA
Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition
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Python Editorial Review
Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition offers a practical gateway into the realms of data analysis and machine learning for those with a foundational knowledge of programming. The book meticulously guides readers through essential phases like data cleaning, model evaluation, and visualization in a coherent and engaging narrative, which resonates well with learners seeking to bridge theory and application. The text emphasizes real-world applications, making it an invaluable resource for Python students looking to tackle genuine data challenges. Some reviews note that while it skillfully illustrates concepts and techniques, it might not deeply explore theoretical frameworks, catering to a more application-focused audience.
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Прос
- Clear presentation of basic data concepts
- Well-structured around real-world projects
- Suitable for programmers with basic skills
- Engaging narrative style facilitates learning
- Covers essential coding techniques in context
Цонс
- Some chapters may feel longer than necessary
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RSD 6491
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Карактеристике и предности
- Gain hands-on experience of Python programming with industry-standard machine learning techniques
- Learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn
- Master the process of data exploration, processing, and fitting algorithms
- Deliver business insights by presenting clear, convincing conclusions
- Recommended for those keen to advance career by using data analysis and predictive modeling
- Includes updated content on XGBoost, SHAP values, algorithmic fairness, and ethical concerns of deploying models
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