Top 10 Best Probabilistic Machine Learning

of November 2024
1
Best ChoiceBest Choice
Probabilistic Machine Learning: An Introduction (Adaptive Computation and
10
Exceptional
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2
Best ValueBest Value
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine
The MIT Press
The MIT Press
9.9
Exceptional
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3
Probabilistic Machine Learning for Civil Engineers (The MIT Press)
The MIT Press
The MIT Press
9.8
Exceptional
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4
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability
Manning Publications
Manning Publications
9.7
Exceptional
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5
Machine Learning: A Bayesian and Optimization Perspective
Academic Press
Academic Press
9.6
Exceptional
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6
Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann
9.5
Excellent
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7
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
9.4
Excellent
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8
Data Science on AWS: Implementing End-to-End, Continuous AI and Machine
O'Reilly Media
O'Reilly Media
9.3
Excellent
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9
Pattern Recognition and Machine Learning (Information Science and Statistics)
Springer
Springer
9.2
Excellent
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10
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation
The MIT Press
The MIT Press
9.1
Excellent
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About Probabilistic Machine Learning

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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Mit Press.

Probabilistic Machine Learning for Civil Engineers (The MIT Press)

Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability

Machine Learning: A Bayesian and Optimization Perspective

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

Pattern Recognition and Machine Learning (Information Science and Statistics)

Springer.

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

MIT Press MA.
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