May 1, 2024
Updated May 12, 2025
18 minute read
Machine Learning Models: Understanding the Core of Modern AI
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Find a path to becoming a Machine Learning Models. Learn more at:
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Reading list
We've selected 35 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning Models.
A comprehensive guide covering machine learning and deep learning with Python, this book serves as both a tutorial and a reference. It delves into essential techniques with clear explanations and practical examples using scikit-learn and TensorFlow 2. It helps solidify understanding by explaining the principles behind the models.
Highly relevant to contemporary ML topics, this book focuses on transformers, a key architecture in modern NLP. It provides a practical guide to using the Hugging Face Transformers library for various NLP tasks. It's essential for those interested in state-of-the-art text-based models.
Focusing on the entire lifecycle of machine learning systems, this book covers design, engineering, and product aspects. It addresses contemporary challenges in deploying and managing ML models in real-world applications, highly relevant for aspiring and current ML engineers.
Provides a comprehensive overview of deep learning, a subfield of machine learning that has seen rapid growth in recent years. It is written by three leading researchers in the field and is suitable for readers with a background in mathematics and computer science.
Provides a comprehensive overview of machine learning, covering both the theoretical foundations and practical applications. It is written by Andrew Ng, a leading researcher in the field of machine learning, and is suitable for readers with a background in mathematics and computer science.
Provides a comprehensive overview of reinforcement learning, a subfield of machine learning that deals with learning how to take actions in an environment in order to maximize a reward. It is written by two leading researchers in the field and is suitable for readers with a background in mathematics and computer science.
Provides a practical introduction to machine learning using Python and the scikit-learn library. It's well-suited for beginners and those with some Python experience looking to build their own machine learning solutions. It emphasizes the practical aspects of using algorithms over the underlying mathematics.
Focuses on the practical aspects of building and deploying machine learning systems in production. It covers the process, best practices, and challenges involved in MLOps, aligning well with several of the listed course titles. It's highly relevant for working professionals.
A classic in the field, this book offers a comprehensive introduction to pattern recognition and machine learning from a probabilistic perspective. It covers a wide range of algorithms and concepts with mathematical rigor. It's an excellent resource for developing a deeper theoretical understanding.
This textbook offers a principled introduction to machine learning, bridging the gap between theory and algorithms. It provides a solid theoretical account of fundamental ideas and mathematical derivations. It's a good resource for a deeper understanding of how algorithms work.
Feature engineering critical step in the machine learning pipeline. focuses specifically on techniques for creating effective features from raw data. It's a practical guide that complements the theoretical understanding of models.
Cornerstone in statistical learning, providing a comprehensive overview of methods for data mining, inference, and prediction. While statistically focused, it covers many fundamental machine learning concepts. It's a valuable reference for both theoretical and applied aspects.
Provides a comprehensive overview of machine learning for data streams, a subfield of machine learning that deals with learning from data that is continuously arriving and changing. It is written by four leading researchers in the field and is suitable for readers with a background in machine learning.
Provides a comprehensive overview of machine learning, covering both the theoretical foundations and practical applications. It is written by Tom M. Mitchell, a leading researcher in the field of machine learning, and is suitable for readers with a background in computer science.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers a wide range of machine learning algorithms and techniques, and is suitable for readers with a background in mathematics and statistics.
A foundational text in reinforcement learning, this book covers the fundamental concepts and algorithms in this area of machine learning. It's considered a classic and is crucial for understanding how agents can learn through interaction with an environment.
Takes a unique approach by teaching the fundamentals of deep learning by building neural networks from scratch using Python and NumPy. It focuses on understanding the underlying mechanisms rather than just using high-level frameworks. It's great for solidifying the core concepts.
Considered a classic textbook in machine learning, this book provides a foundational introduction to the field. While older, it covers many core concepts and algorithms that are still relevant today and is valuable for historical context and fundamental understanding.
Provides a practical introduction to machine learning for non-programmers. It covers a wide range of machine learning algorithms and techniques, and is suitable for readers with no prior experience in programming.
Provides a practical introduction to machine learning using the Python programming language. It covers a wide range of machine learning algorithms and techniques, and is suitable for readers with a background in programming.
Provides a practical introduction to machine learning using the Scikit-Learn, Keras, and TensorFlow libraries in Python. It covers a wide range of machine learning algorithms and techniques, and is suitable for readers with a background in programming.
Provides a practical introduction to machine learning for non-programmers. It covers a wide range of machine learning algorithms and techniques, and is suitable for readers with no prior experience in programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/9irmtv/machine
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