Python Machine Learning By Example, Fourth Edition, Yuxi (Hayden) Liu, 2024

Python Machine Learning By Example, Fourth Edition, Yuxi (Hayden) Liu, 2024.

   The fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners, and experienced Machine Learning (ML) practitioners who want to learn more advanced techniques like multimodal modeling. This edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts.




Understanding why we need machine learning.
Why do we need machine learning, and why do we want a machine to learn the same way as a human? We can look at it from three main perspectives: maintenance, risk mitigation, and enhanced performance.

First and foremost, of course, computers and robots can work 24/7 and don’t get tired. Machines cost a lot less in the long run. Also, for sophisticated problems that involve a variety of huge datasets or complex calculations, it’s much more justifiable, not to mention intelligent, to let computers do all the work. Machines driven by algorithms that are designed by humans can learn latent rules and inherent patterns, enabling them to carry out tasks effectively.

Learning machines are better suited than humans for tasks that are routine, repetitive, or tedious. Beyond that, automation by machine learning can mitigate risks caused by fatigue or inattention. Self-driving cars, as shown in Figure 1.1, are a great example: a vehicle is capable of navigating by sensing its environment and making decisions without human input. Another example is the use of robotic arms in production lines, which are capable of causing a significant reduction in injuries and costs.

Contents.
Preface.
Chapter 1: Getting Started with Machine Learning and Python.
Chapter 2: Building a Movie Recommendation Engine with Naïve Bayes.
Chapter 3: Predicting Online Ad Click-Through with Tree-Based Algorithms.
Chapter 4: Predicting Online Ad Click-Through with Logistic Regression.
Chapter 5: Predicting Stock Prices with Regression Algorithms.
Chapter 6: Predicting Stock Prices with Artificial Neural Networks.
Chapter 7: Mining the 20 Newsgroups Dataset with Text Analysis Techniques.
Chapter 8: Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling.
Chapter 9: Recognizing Faces with Support Vector Machine.
Chapter 10: Machine Learning Best Practices.
Chapter 11: Categorizing Images of Clothing with Convolutional Neural Networks.
Chapter 12: Making Predictions with Sequences Using Recurrent Neural Networks.
Chapter 13: Advancing Language Understanding and Generation with the Transformer Models.
Chapter 14: Building an Image Search Engine Using CLIP: a Multimodal Approach.
Chapter 15: Making Decisions in Complex Environments with Reinforcement Learning.
Other Books You May Enjoy..
Index.



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