Introduction to Machine Learning with Python: A Guide for Data Scientists
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This book will teach you practical ways to build your own machine learning solutions, even as a beginner.
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Product Details
- Introduction to Machine Learning with Python: A Guide for Data Scientists 1st Edition
- By O'Reilly, a brand known for sharing the knowledge of experts
- Comprehensive guide to machine learning for data scientists
- Provides the necessary skills and understanding for success
- Inspires companies and individuals to innovate and excel
- Helps customers build the innovations that propel the world forward
| Yayu0131ncu0131 | O'Reilly Media |
| Yayu0131nlanma Tarihi | 15 Kasu0131m 2016 |
| Basku0131 | 1. |
| Dil | u0130ngilizce |
| Basku0131 Uzunluu011fu | 392 sayfa |
| ISBN-10 | 1449369413 |
| ISBN-13 | 978-1449369415 |
| Boyutlar | 17.53 x 1.78 x 23.11 cm |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners wanting practical skills in machine learning using Python through clear examples and hands-on exercises.
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Python Programmers
Great for those familiar with Python who wish to expand their knowledge into machine learning concepts and applications.
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Academic Researchers
Suitable for researchers looking for a foundational text on machine learning to complement their theoretical knowledge with practical coding.
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Complete Beginners
Not ideal for users without any programming experience, as it assumes prior knowledge of Python syntax and basic concepts.
Product Description
Introduction to Machine Learning with Python: A Guide for Data Scientists
About This Item
Product Description: Unlock the power of machine learning with "Introduction to Machine Learning with Python: A Guide for Data Scientists (1st Edition)". Designed specifically for aspiring data scientists, this comprehensive guide serves as a valuable resource to master the essential concepts and techniques of machine learning using Python. Whether you are a beginner or an experienced professional in the field of data science, this book is packed with valuable insights and practical examples to enhance your understanding of machine learning algorithms, models, and techniques. With a focus on Python for data science, you'll learn how to leverage its powerful libraries such as NumPy, Pandas, and Scikit-learn to analyze and manipulate data efficiently. "Introduction to Machine Learning with Python" covers a wide range of topics including data analysis, data visualization, predictive analytics, big data analytics, and artificial intelligence.
You'll gain a deeper understanding of key concepts such as deep learning, natural language processing, reinforcement learning, supervised and unsupervised learning, classification algorithms, regression analysis, and more. Harness the full potential of machine learning by exploring advanced concepts including feature engineering, model evaluation, cross-validation, dimensionality reduction, clustering algorithms, neural networks, support vector machines, random forests, decision trees, ensemble learning, anomaly detection, time series analysis, recommendation systems, sentiment analysis, and data cleaning. Written by industry experts, this 1st edition guide is an invaluable resource for data scientists who want to stay ahead in the rapidly evolving field of machine learning. Maximize your career opportunities and develop your skills to solve complex data challenges with confidence. Take your first step towards becoming a proficient data scientist with "Introduction to Machine Learning with Python: A Guide for Data Scientists (1st Edition)". Order your copy today and embark on a transformative journey into the world of machine learning and Python for data science.
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Features & Benefits
- Learn how to create successful machine learning applications with Python and the scikit-learn library.
- Discover the fundamental concepts and applications of machine learning.
- Understand the advantages and shortcomings of widely used machine learning algorithms.
- Explore advanced methods for model evaluation and parameter tuning.
- Get suggestions for improving your machine learning and data science skills.
- Learn how to represent data processed by machine learning, including which data aspects to focus on.
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