Machine Learning with Python
Machine Learning with Python
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GBPMachine 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.
- 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
Product details
February 2025Paperback
9781009170246
850 pages
242 × 155 mm
Not yet published - available from February 2025
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.
