Python Machine Learning By Example.
Implement machine learning algorithms and techniques to build intelligent systems
2nd Edition
Published on 3. June 2024
382 pages
978-1-78961-755-9 (ISBN)
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Yuxi (Hayden) Liu
Python Machine Learning by Example
Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
E-Book
09/2024
3rd Edition
Packt Publishing
€23.49
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Yuxi (Hayden) Liu
Python Machine Learning By Example
Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition
Book
02/2019
2nd Edition
Packt Publishing
€46.80
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Previous edition
Yuxi (Hayden) Liu | Ivan Idris
Python Machine Learning By Example
The easiest way to get into machine learning
E-Book
06/2024
1st Edition
Packt Publishing Limited
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€39.59
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Person
Liu Yuxi (Hayden) :
Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.
Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.
Content
Table of Contents - Getting Started with Machine Learning and Python
- Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
- Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
- Detecting Spam Email with Naive Bayes
- Classifying News Topic with Support Vector Machine
- Predicting Online Ads Click-through with Tree-Based Algorithms
- Predicting Online Ads Click-through with Logistic Regression
- Scaling Up Prediction to Terabyte Click Logs
- Stock Price Prediction with Regression Algorithms
- Machine Learning Best Practices
- Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
- Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
- Detecting Spam Email with Naive Bayes
- Classifying News Topic with Support Vector Machine
- Predicting Online Ads Click-through with Tree-Based Algorithms
- Predicting Online Ads Click-through with Logistic Regression
- Scaling Up Prediction to Terabyte Click Logs
- Stock Price Prediction with Regression Algorithms
- Machine Learning Best Practices
