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Python Machine Learning By Example

Python Machine Learning By Example - Fourth Edition

By : Yuxi (Hayden) Liu
4.9 (8)
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Python Machine Learning By Example

Python Machine Learning By Example

4.9 (8)
By: Yuxi (Hayden) Liu

Overview of this book

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.
Table of Contents (18 chapters)
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16
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Index

Best practices in the data preparation stage

No machine learning system can be built without data. Therefore, data collection should be our first focus.

Best practice 1 – Completely understanding the project goal

Before starting to collect data, we should make sure that the goal of the project and the business problem are completely understood, as this will guide us on what data sources to look into, and where sufficient domain knowledge and expertise is also required. For example, in a previous chapter, Chapter 5, Predicting Stock Prices with Regression Algorithms, our goal was to predict the future prices of the stock index, so we first collected data on its past performance, instead of the past performance of an irrelevant European stock. In Chapter 3, Predicting Online Ad Click-Through with Tree-Based Algorithms, for example, the business problem was to optimize advertising, targeting efficiency measured by click-through rate, so we collected the clickstream data...

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