Introduction to Machine Learning with Python: A Guide for Data Scientists
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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you’ll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skillsProduct details Publisher: O’Reilly Media; 1 edition (October 21, 2016) ISBN-10: 1449369413 ISBN-13: 978-1449369415
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Introduction to Machine Learning with Python: A Guide for Data Scientists
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All orders placed on our website are processed within 2-4 business days, from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Orders received after our daily cut-off time of 10:00 PM PT will be processed on the next business day. Please note that we do not process orders on weekends or public holidays.
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The choice of carrier is determined by factors such as destination, weight, and delivery timeframe to provide optimal service.
Shipping Rates and Fees
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Free shipping is provided for all orders over $199.
-
Orders under $199 will incur a flat-rate shipping fee of $7.99.
-
All orders shipped within the United States will be subject to a sales tax charge of 5%.
Estimated Delivery Time
Once shipped, orders typically arrive within 6 to 10 business days. Our delivery times are from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Please allow additional time for deliveries to remote or rural locations.
Shipping Restrictions
Zetlly currently ships exclusively within the United States. At present, we do not offer international shipping or deliveries to P.O. boxes or APO/FPO addresses. Orders placed with addresses outside our designated delivery areas will be canceled, and refunds will be processed accordingly.
Tracking Your Order
Upon shipment, customers will receive a confirmation email containing tracking information. You can track your order directly through the provided tracking link or by visiting the carrier’s official website:
Please allow up to 48 hours for tracking information to update in the carrier’s system.
Eligibility for Returns and Exchanges
We accept returns and exchanges within 30 days from the date your order is delivered. Items must be unused, in the original condition, and accompanied by the original packaging and receipt or proof of purchase.
How to Return or Exchange an Item
To initiate a return or exchange, please follow these steps:
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Contact our customer support at contact@zetlly.com with your order number and reason for return or exchange.
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Our team will respond within 24 hours to provide detailed instructions, including the specific Return Address for your shipment.
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Package your item securely and include all original packaging and proof of purchase.
Return shipments should be sent to: Blanq LLC 1201 South Hope Street Apt 2413, Los Angeles, CA 90015, USA
Return Conditions
-
Items must be returned in their original condition, unworn, undamaged, and complete with all original packaging and documentation.
-
Items returned without prior authorization or not meeting the above conditions may not qualify for a refund or exchange.
Return Shipping Costs
Customers are responsible for return shipping costs unless the return is due to our error or a defective product. We recommend using a trackable shipping service to ensure your return reaches us safely.
Non-Returnable Items
The following items cannot be returned:
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-
Personalized or customized items
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Accepted Payment Methods
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-
PayPal: Easily pay through your PayPal account, benefiting from secure transactions and buyer protection.
-
Stripe: Pay securely using major credit and debit cards including Visa, MasterCard, American Express, and Discover via Stripe’s encrypted payment gateway.
Payment Security
At Zetlly, your security is our utmost priority. We utilize advanced encryption technologies and robust security protocols provided by PayPal and Stripe. All payment information entered on our site is encrypted using Secure Socket Layer (SSL) technology, ensuring your financial information remains private and secure throughout the transaction process.
Zetlly does not store any credit card or sensitive financial information directly on our servers, further enhancing the security and protection of your personal data.
Payment Process and Confirmation
Upon placing an order, your chosen payment method (PayPal or Stripe) will immediately process the transaction. You will receive an automated confirmation email shortly after your payment has been successfully completed, detailing your transaction and order summary.
Please retain this confirmation email for your records and reference in case of any inquiries or disputes.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You�ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas M�ller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you�ll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills
ISBN-13
978-1449369873
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