FAISAL BIN BASHA
P(A|B) = P(B|A)·P(A)/P(B) σ² = E[(X−μ)²] L(θ) = ∏P(xᵢ|θ) VOLUME I MACHINE LEARNING with STATISTICS PROBABILITY · INFERENCE · PREDICTION FAISAL BIN BASHA
A Quantitative Guide

MACHINE LEARNING with STATISTICS

— Where statistics meets the model. Where models meet production. —

BY FAISAL BIN BASHA

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Statistics isn't an afterthought in machine learning. It is the foundation.

What you'll learn

1

Probability foundations — distributions, expectation, and information theory.

2

Statistical inference — estimation, hypothesis testing, confidence intervals.

3

Bayesian reasoning under uncertainty and posterior inference.

4

Linear models and their generalisations across the GLM family.

5

Bias–variance tradeoffs and rigorous model selection.

6

Bootstrapping, cross-validation, and honest evaluation.

7

Probabilistic deep learning and uncertainty quantification.

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About the author

Faisal Bin Basha

Faisal Bin Basha is an AI and DevOps engineer with a multidisciplinary background spanning artificial intelligence, cloud computing, cybersecurity, and enterprise infrastructure.

He holds a Master of Science in Artificial Intelligence and has pursued advanced studies in computer science and machine learning through the Georgia Institute of Technology online master's program. Professional certifications include AWS Machine Learning Specialty, AWS Security Specialty, AWS Solutions Architect Associate, AWS Cloud Practitioner, and Microsoft Azure Solutions Architect Expert.

His work focuses on practical, real-world applications of machine learning, MLOps, cloud-native architectures, observability, and AI-powered operational intelligence — building strong bridges between traditional IT operations and modern intelligent systems.

github.com/faisalbasha1982 linkedin.com/in/faisalbinbasha faisalbasha.ceh@gmail.com

Ready to dig in?

Probability · Inference · Prediction — in one volume.

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