Machine Learning from Scratch Book
The “Machine Learning from Scratch Book” delves into the fundamentals of building machine learning models without relying on pre-existing libraries. It covers topics such as data augmentation, deterministic bridge engineering, and the challenges of generative AI in enterprise settings. The book emphasizes understanding the core concepts of machine learning, including vector embeddings, multi-tenancy architecture, and retrieval-augmented generation. By exploring real-world applications and practical examples, it aims to provide readers with a comprehensive understanding of machine learning principles and techniques.
Implementation
This section demonstrates how to fit a regression model in Python in practice. The two most common packages for fitting regression models in Python are scikit-learn and statsmodels . Both methods are...
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Construction
This section demonstrates constructions of bagging models, random forests, and boosting for classification and regression. Each of these relies on the decision tree constructions from the last chapte...
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Table of Contents
Ordinary Linear Regression The Loss-Minimization Perspective The Likelihood-Maximization Perspective Linear Regression Extensions Regularized Regression (Ridge and Lasso) Bayesian Regression Generali...
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Conventions and Notation
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Concept
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Math
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Probability
Many machine learning methods are rooted in probability theory. Probabilistic methods in this book include linear regression , Bayesian regression , and generative classifiers . This section covers t...
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Common Methods
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Datasets
The examples in this book use several datasets that are available either through scikit-learn or seaboarn . Those datasets are described briefly below. Boston Housing The Boston housing dataset conta...
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