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Visual Maths for ML: ML Concepts Before Mathematics
Understand learning before the equations.
Understand learning before the equations. Annotated diagrams and worked examples. Includes PDF and fixed-layout EPUB.
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EPUB
About
About the Book
Understand learning before the equations. Build intuition through annotated diagrams, small worked examples and step-by-step explanations.
WHAT YOU WILL LEARN
1. What learning means
Examples, inputs and targets; regression and delivery-time predictions; learned weights and biases; prediction errors and losses.
2. How models improve
Slopes, gradient-descent updates and tracing the effect of a small change before formal differentiation.
3. Neural networks
Units, weighted sums, biases, activation rules and connected layers; following calculations through a small network.
4. Probabilities and scores
Exponentials, logarithms, predicted probabilities, softmax and why scoring confidence matters beyond the final label.
5. Text and attention
Embeddings as number lists, contextual meaning, attention shares and a complete weighted information-mixing example.
6. Learning from probabilities
Conditional probability, Naive Bayes email classification, generative modeling, likelihood, priors and posterior updating.
7. Evaluate fairly
Unseen data, calibration, predictive evaluation, randomized A/B experiments, underfitting, overfitting and cross-validation.
8. Reduce and represent
PCA, centering and projection scores; SVD and retaining strong patterns in a matrix.
9. Connect the journey
Worked delivery, neuron and PCA checks, six additional practice situations, answer routes and a map to the mathematics behind each idea.
WHAT IS INCLUDED
38-page PDF: a cover plus 37 illustrated lesson pages. A fixed-layout EPUB preserves the same visual pages and includes navigation. The lesson text is embedded in images and does not reflow; phones may require zooming.
A free opening-lesson PDF lets you preview the teaching style before buying.
WHO IT IS FOR
Readers who want a visual, example-led introduction or refresher. Follow the calculations and practice checks at your own pace.
Pramod Kumar Tyagi · www.bitmonkey.ai
© 2026 Bitmonkey IT Services Pvt Ltd. All rights reserved.
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Author
About the Author
Pramod Kumar 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 illustrated books use annotated diagrams, worked examples and practical scenarios to help readers build intuition for programming and the mathematics behind machine learning.
Explore his work at www.bitmonkey.ai.
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