MACHINE LEARNING make your own recommender system
Launch into machine learning with our course and learn to create advanced recommender systems, ensuring ethical use and maximizing user satisfaction. Key Features Navigate Scikit-Learn effortlessly Create advanced recommender systems Understand ethical AI development Book Description With an introdu...
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| Format: | eBook |
| Language: | English |
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Birmingham, UK
Packt Publishing Ltd.
2024
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | Launch into machine learning with our course and learn to create advanced recommender systems, ensuring ethical use and maximizing user satisfaction. Key Features Navigate Scikit-Learn effortlessly Create advanced recommender systems Understand ethical AI development Book Description With an introductory overview, the course prepares you for a deep dive into the practical application of Scikit-Learn and the datasets that bring theories to life. From the basics of machine learning to the intricate details of setting up a sandbox environment, this course covers the essential groundwork for any aspiring data scientist. The course focuses on developing your skills in working with data, implementing data reduction techniques, and understanding the intricacies of item-based and user-based collaborative filtering, along with content-based filtering. These core methodologies are crucial for creating accurate and efficient recommender systems that cater to the unique preferences of users. What you will learn Build data-driven recommender systems Implement collaborative filtering techniques Apply content-based filtering methods Evaluate recommender system performance Address privacy and ethical considerations Anticipate future recommender system trends Who this book is for This course is ideal for aspiring data scientists and technical professionals with a basic understanding of Python programming and a keen interest in machine learning. This course lays the groundwork for those looking to specialize in building sophisticated recommender systems Practical examples and evaluations further solidify your learning, making complex concepts accessible and manageable. The course wraps up by addressing the critical topics of privacy, ethics in machine learning, and the exciting future of recommender systems. This holistic approach ensures that you not only gain technical proficiency but also consider the broader implications of your work in this field. With a final look at further resources, your journey into machine learning and recommender systems is just beginning, armed with the knowledge and tools to explore new horizons. |
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| Physical Description: | 1 online resource |
| ISBN: | 1835882064 |