Machine Learning with Python
Principles and Practical Techniques
- Author: Parteek Bhatia, Thapar University, India
- Publication planned for: November 2024
- availability: Not yet published - available from November 2024
- format: Paperback
- isbn: 9781009170246
Paperback
-
Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms, and then illustrates each concept with its detailed implementation in Python to allow beginners to effectively implement the principles in real-world applications. All major techniques, such as regression, classification, clustering, deep learning, and association mining, have been illustrated using step-by-step coding instructions to help inculcate a 'learning by doing' approach. The book has no prerequisites, and covers the subject from the ground up, including a detailed introductory chapter on the Python language. As such, it is going to be a valuable resource not only for students of computer science, but also for anyone looking for a foundation in the subject, as well as professionals looking for a ready reckoner.
Read more- Algorithms are explained in detail with examples with a step-by-step approach to make learning easy and simple, assuming no previously existing knowledge
- GitHub resources that provide access to datasets, sample code, and examples have been included in each chapter
- Advanced topics like Deep Learning, Convolutional Neural Networks, and Recurrent Neural Networks have been covered extensively
- An online supplements package includes a solutions manual and lecture slides for instructors, and further online reading and a chapter-wise list of project ideas for students
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Publication planned for: November 2024
- format: Paperback
- isbn: 9781009170246
- length: 850 pages
- dimensions: 242 x 155 mm
- availability: Not yet published - available from November 2024
Table of Contents
Acknowledgements
Preface
Chapter 1. Beginning with Machine Learning
Chapter 2. Introduction to Python
Chapter 3. Data Pre-processing
Chapter 4. Implementing Data Pre-processing in Python
Chapter 5. Simple Linear Regression
Chapter 6. Implementing Simple Linear Regression
Chapter 7. Multiple Linear Regression and Polynomial Linear Regression
Chapter 8. Implementing Multiple Linear Regression and Polynomial Linear Regression
Chapter 9. Classification
Chapter 10. Support Vector Machine Classifier
Chapter 11. Implementing Classification
Chapter 12. Clustering
Chapter 13. Implementing Clustering
Chapter 14. Association Mining
Chapter 15. Implementing Association Mining
Chapter 16. Artificial Neural Network
Chapter 17. Implementing the Artificial Neural Network
Chapter 18. Deep Learning and Convolutional Neural Network
Chapter 19. Implementing Convolutional Neural Network
Chapter 20. Recurrent Neural Network
Chapter 21. Implementing Recurrent Neural Network
Chapter 22. Genetic Algorithm for Machine Learning
Index.
Related Books
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email [email protected]
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×