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Python Machine Learning - Third Edition

You're reading from  Python Machine Learning - Third Edition

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781789955750
Pages 772 pages
Edition 3rd Edition
Languages
Concepts
Authors (2):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka
LinkedIn
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
See other products by Sebastian Raschka
Vahid Mirjalili Vahid Mirjalili
Profile icon Vahid Mirjalili
LinkedIn
Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
See other products by Vahid Mirjalili
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Table of Contents (21) Chapters

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Simple Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using scikit-learn 4. Building Good Training Datasets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper – The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data Using Recurrent Neural Networks 17. Generative Adversarial Networks for Synthesizing New Data 18. Reinforcement Learning for Decision Making in Complex Environments 19. Other Books You May Enjoy 20. Index

Turning the movie review classifier into a web application

Now that we are somewhat familiar with the basics of Flask web development, let's advance to the next step and implement our movie classifier into a web application. In this section, we will develop a web application that will first prompt a user to enter a movie review, as shown in the following screenshot:

After the review has been submitted, the user will see a new page that shows the predicted class label and the probability of the prediction. Furthermore, the user will be able to provide feedback about this prediction by clicking on the Correct or Incorrect button, as shown in the following screenshot:

If a user clicked on either the Correct or Incorrect button, our classification model will be updated with respect to the user's feedback. Furthermore, we will also store the movie review text provided by the user, as well as the suggested class label, which can be inferred from the button click,...

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Authors (2)

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Profile icon Sebastian Raschka
LinkedIn
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
See other products by Sebastian Raschka
Profile icon Vahid Mirjalili
LinkedIn
Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
See other products by Vahid Mirjalili
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