Machine Learning Foundations, Volume 1 Supervised Learning 1st Edition By Roi Yehoshua
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Machine Learning Foundations, Volume 1 Supervised Learning 1st Edition By Roi Yehoshua

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722
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9.88 MB
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Digital PDF
Course
Business
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eBook[PDF]
About this ebook
The Machine Learning Foundations, Volume 1: Supervised Learning(1st Edition) by Roi Yehoshua, PhD—published by Addison-Wesley Professional—is a comprehensive textbook designed to balance mathematical rigor with hands-on Python programming. [1, 2]
The book covers the essential concepts, foundational algorithms, workflow pipelines, and practical tools needed to master supervised learning. [1, 2]
The core topics and chapters covered in this volume include:
1. Core Machine Learning Concepts & Foundations
  • Theoretical Frameworks: Model capacity, empirical risk minimization, generalization error, and the data-generating process.
  • The Bias-Variance Tradeoff: Understanding underfitting versus overfitting, regularization techniques, and optimization.
  • Learning Tasks: Formal definitions and execution of classification, regression, multi-label, and multi-output problems. [1, 2, 3, 4]
2. Foundational & Linear Algorithms
  • Linear Regression: Foundational regression analysis, optimization, and parameter estimation.
  • Logistic Regression: Classification metrics, baseline probabilistic modeling, and decision boundaries.
  • Instance-Based & Probabilistic Models: K-Nearest Neighbors (KNN) and Naive Bayes classifiers. [1, 2, 3]
3. Tree-Based Models & Ensemble Methods
  • Decision Trees: Building tree architectures from scratch for both classification and regression tasks.
  • Ensemble Learning: Implementation of bagging, Random Forests, and AdaBoost.
  • Advanced Boosting Frameworks: Deep dives into Gradient Boosting, XGBoost, LightGBM, and CatBoost. [1, 2, 3]
4. Advanced Vector Machines
  • Support Vector Machines (SVMs): Maximum margin classifiers and structural risk minimization.
  • Kernel Methods: Transforming non-linearly separable data into higher dimensions. [1, 2]
5. Practical Implementation, Pipelines & Tools
  • Hands-on Frameworks: Utilizing industry-standard libraries like Scikit-Learn, XGBoost, and NLTK.
  • Data Processing Pipeline: Comprehensive strategies for feature engineering, data preparation, data preprocessing, and handling diverse data types like tabular data, text, and images.
  • Model Optimization: Evaluating performance metrics, setting up cross-validation, hyperparameter tuning, and model calibration.
  • Custom Architectures: Guidance on building custom estimators using the Scikit-Learn Estimator API. [1, 2, 3]

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Topics

Advanced Boosting Frameworks: Deep dives into Gradient Boosting Advanced Vector Machines Core Machine Learning Concepts & Foundations Theoretical Frameworks: Model capacity Custom Architectures: Guidance on building custom estimators using the Scikit-Learn Estimator API Data Processing Pipeline: Comprehensive strategies for feature engineering Decision Trees: Building tree architectures from scratch for both classification and regression tasks Ensemble Learning: Implementation of bagging Foundational & Linear Algorithms Hands-on Frameworks: Utilizing industry-standard libraries like Scikit-Learn Instance-Based & Probabilistic Models: K-Nearest Neighbors (KNN) and Naive Bayes classifiers ISBN-10: 0135337925 ISBN-13: 978-0135337929 ISBN: 978-0135337851 Kernel Methods: Transforming non-linearly separable data into higher dimensions Learning Tasks: Formal definitions and execution of classification Linear Regression: Foundational regression analysis Logistic Regression: Classification metrics Model Optimization: Evaluating performance metrics Pipelines & Tools Practical Implementation Support Vector Machines (SVMs): Maximum margin classifiers and structural risk minimization The Bias-Variance Tradeoff: Understanding underfitting versus overfitting Tree-Based Models & Ensemble Methods XGBoost
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