based on Python 3.11

README.md

@@ -1,24 +1,24 @@
 ## Python Machine Learning (3rd Ed.) Code Repository
 
-[![Python 3.6](https://img.shields.io/badge/Python-3.7-blue.svg)](#)
+[![Python 3.11](https://img.shields.io/badge/Python-3.11-blue.svg)](#)
 [![License](https://img.shields.io/badge/Code%20License-MIT-blue.svg)](LICENSE.txt)
 
 Code repositories for the 1st and 2nd edition are available at
 
 - https://github.com/rasbt/python-machine-learning-book and
 - https://github.com/rasbt/python-machine-learning-book-2nd-edition
 
-**Python Machine Learning, 3rd Ed.**  
+**Python Machine Learning, 3rd Ed.**
 
 to be published December 12th, 2019
 
-Paperback: 770 pages  
-Publisher: Packt Publishing  
+Paperback: 770 pages
+Publisher: Packt Publishing
 Language: English
 
-ISBN-10: 1789955750   
-ISBN-13: 978-1789955750  
-Kindle ASIN: B07VBLX2W7 
+ISBN-10: 1789955750
+ISBN-13: 978-1789955750
+Kindle ASIN: B07VBLX2W7
 
 [<img src="./.other/cover_1.jpg" width="248">](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750/)
 
@@ -34,42 +34,42 @@ Kindle ASIN: B07VBLX2W7
 
 **Helpful installation and setup instructions can be found in the [README.md file of Chapter 1](ch01/README.md)**
 
-**Please note that these are just the code examples accompanying the book, which we uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**   
+**Please note that these are just the code examples accompanying the book, which we uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**
 
 
-1. Machine Learning - Giving Computers the Ability to Learn from Data [[open dir](ch01)] 
-2. Training Machine Learning Algorithms for Classification [[open dir](ch02)] 
-3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](ch03)] 
-4. Building Good Training Sets – Data Pre-Processing [[open dir](ch04)] 
-5. Compressing Data via Dimensionality Reduction [[open dir](ch05)] 
+1. Machine Learning - Giving Computers the Ability to Learn from Data [[open dir](ch01)]
+2. Training Machine Learning Algorithms for Classification [[open dir](ch02)]
+3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](ch03)]
+4. Building Good Training Sets – Data Pre-Processing [[open dir](ch04)]
+5. Compressing Data via Dimensionality Reduction [[open dir](ch05)]
 6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](ch06)]
-7. Combining Different Models for Ensemble Learning [[open dir](ch07)] 
-8. Applying Machine Learning to Sentiment Analysis [[open dir](ch08)] 
-9. Embedding a Machine Learning Model into a Web Application [[open dir](ch09)]  
-10. Predicting Continuous Target Variables with Regression Analysis [[open dir](ch10)] 
-11. Working with Unlabeled Data – Clustering Analysis [[open dir](ch11)] 
-12. Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](ch12)] 
-13. Parallelizing Neural Network Training with TensorFlow [[open dir](ch13)] 
-14. Going Deeper: The Mechanics of TensorFlow [[open dir](ch14)] 
-15. Classifying Images with Deep Convolutional Neural Networks [[open dir](ch15)]  
-16. Modeling Sequential Data Using Recurrent Neural Networks [[open dir](ch16)] 
-17. Generative Adversarial Networks for Synthesizing New Data [[open dir](ch17)]  
-18. Reinforcement Learning for Decision Making in Complex Environments [[open dir](ch18)] 
-
-
---- 
+7. Combining Different Models for Ensemble Learning [[open dir](ch07)]
+8. Applying Machine Learning to Sentiment Analysis [[open dir](ch08)]
+9. Embedding a Machine Learning Model into a Web Application [[open dir](ch09)]
+10. Predicting Continuous Target Variables with Regression Analysis [[open dir](ch10)]
+11. Working with Unlabeled Data – Clustering Analysis [[open dir](ch11)]
+12. Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](ch12)]
+13. Parallelizing Neural Network Training with TensorFlow [[open dir](ch13)]
+14. Going Deeper: The Mechanics of TensorFlow [[open dir](ch14)]
+15. Classifying Images with Deep Convolutional Neural Networks [[open dir](ch15)]
+16. Modeling Sequential Data Using Recurrent Neural Networks [[open dir](ch16)]
+17. Generative Adversarial Networks for Synthesizing New Data [[open dir](ch17)]
+18. Reinforcement Learning for Decision Making in Complex Environments [[open dir](ch18)]
+
+
+---
 
 <br>
 <br>
 
 Raschka, Sebastian, and Vahid Mirjalili. *Python Machine Learning, 3rd Ed*. Packt Publishing, 2019.
 
-    @book{RaschkaMirjalili2019,  
-    address = {Birmingham, UK},  
-    author = {Raschka, Sebastian and Mirjalili, Vahid},  
-    edition = {3},  
-    isbn = {978-1789955750},   
-    publisher = {Packt Publishing},  
-    title = {{Python Machine Learning, 3rd Ed.}},  
-    year = {2019}  
+    @book{RaschkaMirjalili2019,
+    address = {Birmingham, UK},
+    author = {Raschka, Sebastian and Mirjalili, Vahid},
+    edition = {3},
+    isbn = {978-1789955750},
+    publisher = {Packt Publishing},
+    title = {{Python Machine Learning, 3rd Ed.}},
+    year = {2019}
     }