Machine Learning Yearning
An introductory book about developing ML algorithms
Book • 2021
Andrew Ng's "Machine Learning Yearning" is a concise guide focused on practical strategies for building successful machine learning systems.
The book emphasizes a structured approach to problem-solving, guiding readers through crucial steps like data collection, model selection, and evaluation.
Ng stresses the importance of iterative development and experimentation, encouraging readers to learn from their mistakes and refine their models over time.
The book's clear explanations and practical advice make it accessible to both beginners and experienced practitioners.
It serves as a valuable resource for anyone seeking to improve their machine learning skills and build effective solutions.
The book emphasizes a structured approach to problem-solving, guiding readers through crucial steps like data collection, model selection, and evaluation.
Ng stresses the importance of iterative development and experimentation, encouraging readers to learn from their mistakes and refine their models over time.
The book's clear explanations and practical advice make it accessible to both beginners and experienced practitioners.
It serves as a valuable resource for anyone seeking to improve their machine learning skills and build effective solutions.
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