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Visual Maths for ML: Probability
An illustrated guide from probability basics to machine learning
This book is 100% completeLast updated on 2026-10-18
Make probability visual—from coin flips to an email spam filter. Learn with annotated diagrams, worked examples and practice questions. Includes PDF and fixed-layout EPUB.
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Make probability visual. Follow illustrated examples from bags of counters and coin flips to an email filter that estimates spam probabilities.
Learn outcomes and events, probability rules, random variables, expectation, variance, Bernoulli and binomial models, categorical and multinomial models, continuous probability, the normal distribution, Poisson arrivals, exponential waits, conditional probability, Bayes, Naive Bayes, smoothing, sampling, expected cost, calibration and uncertainty.
Includes a 41-page PDF and fixed-layout EPUB: 37 original illustrated lesson and reference pages, a cover, two visual learning maps, and an email-classification case overview. Includes practice questions and answers. Basic school-level maths is enough to begin.
The EPUB preserves the diagrams and includes lesson navigation. Use a reader that supports fixed-layout EPUB; text does not reflow, and phones may require zooming.
Download the free sample to explore the visual learning maps.
By Pramod Tyagi · www.bitmonkey.ai
© 2026 Bitmonkey IT Services Pvt Ltd. All rights reserved.
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About the Author
Pramod Tyagi
Pramod Kumar Tyagi is an AI and digital architect with more than 20 years of experience designing and scaling enterprise technology platforms. He is the founder of Bitmonkey IT Services Pvt Ltd, where his work spans AI products, digital transformation and interactive AI/ML education.
Previously, Pramod served as Principal Architect and Head of Enterprise Integration & AI Centre of Excellence at NTT DATA Services. His experience includes AI and machine learning, cloud architecture, data platforms and intelligent automation across banking, payments, healthcare, retail and supply chain.
He holds an MS in Aerospace Engineering from the Indian Institute of Science (IISc), Bangalore, and is a TOGAF-certified solution architect.
His Visual Maths for ML series uses annotated diagrams, worked examples and practical scenarios to help readers build intuition for the mathematics behind machine learning.
Explore his work at www.bitmonkey.ai.
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