Machine Learning and Its Applications

List of contents

1.Introduction. 2. Machine Learning Basics. 3. Data Pre-Processing. 4. Feature Extraction. 5. Data Mining Algorithms. 6.Supervised Learning. 7. Clustering. 8. Semi-Supervised Learning. 9. Learning Techniques. 10. Association Rules. 11. DeepLearning. 12. Predictive Analytics. 13. Machine Learning Applications. 14. Ethical Considerations. 15. Future Development.

About the author

Biography:
Peter Wlodarczak is an IT consultant in Data Analytics and Machine Learning. Born in Basel, Switzerland, he holds a Master degree and a PhD from the University of Southern Queensland, Australia. He has many years of experience in large software engineering and data analysis projects. He has published more than 20 papers and book chapters in this area and has presented his work on many conferences. His research interests include among other Machine Learning, eHealth and Bio computing.

Summary

This book describes Machine Learning techniques and algorithms that have been used in recent real-world application. It provides an introduction to Machine Learning, describes the most widely used techniques and methods. It also covers Deep Learning and related areas such as function approximation or.

Product details

• Authors: Peter Wlodarczak, Peter (University of Southern Queensla Wlodarczak
• Assisted by: Peter Wlodarczak (Editor)
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 30.06.2021
• EAN: 9781032086774
• ISBN: 978-1-0-3208677-4
• No. of pages: 204
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; COMPUTERS / Computer Science, TECHNOLOGY & ENGINEERING / Automation, COMPUTERS / Programming / Algorithms, computer science, algorithms and data structures, Algorithms & data structures, Automatic control engineering