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Probabilistic Machine Learning: An Introduction
Rated 4.08 out of 5 based on 25 customer ratings
25
SKU: 9780262046824
₹10,650.00 Original price was: ₹10,650.00.₹9,052.50Current price is: ₹9,052.50.
Dive into the world of machine learning with “Probabilistic Machine Learning: An Introduction” by Kevin P. Murphy. This comprehensive guide offers a detailed exploration of machine learning through the lens of probabilistic modeling and Bayesian decision theory. From linear regression to deep learning, uncover the mathematical foundations and practical applications. Explore cutting-edge topics like transfer learning and unsupervised learning. 9780262046824
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Vaibhav Vivek Sahi –
I found the Bayesian approach fascinating. However, some chapters were hard to follow.
Ishika Arya –
Math is not well explained. However, I was able to understand the concept after some time.
Abhay Kumar Maurya –
It is very verbose. However, the content is solid and insightful
Utkarsh Bansal –
One of the best books on the topic. Clear explanations and relevant examples.
Geetanjali Mukherjee –
Excellent resource for understanding the math behind ML. Highly recommended.
Siddharth Bhandari –
Some topics are not explained clearly. Assumes a good background in statistics.
Himanshu –
Concepts are so well explained, that it becomes the best choice for the person.
Tarun Ohlyan –
I wish the book was more detailed, but overall it serves the purpose
Soumya Ranjan Katha –
A solid intro, but requires existing math knowledge. Some parts felt rushed. Good examples though.
Akanshit Narula –
Dense and challenging. It’s a thorough resource, but not for absolute beginners. Needs patience.
Vaani Kaushik –
A decent introduction, but not the easiest to digest. Expect to reread sections.
Devanshu Agrawal –
A great introduction to the probabilistic approach to ML. Highly recommended!
Harshit Agrawal –
The best ML book I’ve read so far. Clear, concise, and well-organized.
Priyank Agrawal –
A bit overwhelming. Needs more real-world examples to balance the theory.
Rajat Sharan Sethi –
Okay book for understanding fundamentals, but hard to implement by self.
Manisha Kumari Gobind Prajapati –
Great book! Provides a solid foundation in probabilistic machine learning.
Diksha Munjal –
Comprehensive and well-written. It’s now my go-to reference for ML.
Aayush Sharma –
The book dives into the details, this is a complete package for ML. Loved it!
Kapil Yadav –
Too theoretical for my taste. Needed more practical exercises. Okayish book.
Ashtha –
Excellent book! Murphy explains complex concepts clearly. A must-read for anyone serious about ML.
Priya –
Good, but could use more visual aids. Some concepts are hard to visualize.
Jyoti Baghel –
Detailed and rigorous. A valuable addition to any machine learning library.
Drishti Singh –
Difficult to get through, but rewarding. A very thorough introduction.
Mahi Sachdeva –
Good explanations, but the book is quite long. It could be more concise.
Manan Anand –
Comprehensive coverage, but a bit dry. The code examples are helpful. Good resource overall.