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Python Machine Learning by Example

Python Machine Learning by Example

By : Yuxi (Hayden) Liu
4 (20)
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Python Machine Learning by Example

Python Machine Learning by Example

4 (20)
By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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15
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16
Index

Summary

In this chapter, we worked on two NLP projects: sentiment analysis and text generation using RNNs. We started with a detailed explanation of the recurrent mechanism and different RNN structures for different forms of input and output sequences. You also learned how LSTM improves vanilla RNNs. Finally, as a bonus section, we covered the Transformer, a recent state-of-the-art sequential learning model.

In the next chapter, we will focus on the third type of machine learning problem: reinforcement learning. You will learn how the reinforcement learning model learns by interacting with the environment to reach the learning goal.

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