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Python Machine Learning By Example

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
4.9 (9)
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Python Machine Learning By Example

Python Machine Learning By Example

4.9 (9)
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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17
Index

Summary

In this chapter, we continued our journey of supervised learning with SVM. You learned about the mechanics of an SVM, kernel techniques, implementations of SVM, and other important concepts of machine learning classification, including multiclass classification strategies and grid search, as well as useful tips to use an SVM (for example, choosing between kernels and tuning parameters). Then, we finally put into practice what you learned in the form of real-world use cases, including face recognition. You also learned about SVM’s extension to regression, SVR.

In the next chapter, we will review what you have learned so far in this book and examine the best practices of real-world machine learning. The chapter aims to make your learning foolproof and get you ready for the entire machine learning workflow and productionization. This will be a wrap-up of the general machine learning techniques before we move on to more complex topics in the final three chapters.

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