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Supervised learning Algorithms: A student's practical guide
Paperback
Series: AI Course
Publisher: Independently Published
Published: Aug 12 2025
Pages: 168
Weight: 0.89
Height: 0.36 Width: 8.50 Depth: 11.00
Language: English
Overview: What This Book Offers
Supervised Learning Algorithms: A Student's Practical Guide is designed to be a complete, student-friendly resource spanning from fundamentals to real-world applications. It blends solid theory, intuitive explanation, hands-on practice, and deep reflections on ethical and future directions in machine learning.
- Clear progression from foundational math to advanced models, tuned with your preferred Chapter-X / X.X structure for clarity and easy navigation.
- Foundations (Chapters 1-2) ensure students build competency before diving deeper.
- Each concept is explained theoretically and reinforced with intuitive, accessible examples.
- Hands-on Python exercises using libraries like scikit-learn make learning practical and code-first.
- Covers essential models: regression and classification basics, ensemble methods, boosting (XGBoost / LightGBM), and hyperparameter tuning.
- Encourages experimentation and a deep understanding of model behavior.
- Teaches rigorous evation using metrics (accuracy, precision, F1-score), validation strategies, ROC/AUC understanding, and discussions on overfitting vs. generalization.
- Also addresses ethical AI, model interpretability, and fairness concerns.
- In-depth projects: housing price prediction, spam filtering, churn analysis, handwriting recognition.
- Hands-on experience is supplemented with dataset access, real-world insights, and optional competitions or extension ideas.
- Covers current trends and research, interpretability, ethical considerations, and explores how supervised learning fits into the evolving landscape of AI.
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