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Topics Covered
- Data sources
- Knowledge acquisition
- Knowledge representation
- Types of machine learning algorithms
- Decision-making processes
- Value creation
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Provides a hands-on introduction to machine learning, covering a wide range of topics from data preparation to model evaluation. It is written by Andrew Ng, one of the leading researchers in the field of machine learning.
Provides a comprehensive overview of reinforcement learning, a subfield of machine learning that deals with how agents can learn to make decisions in complex environments. It is written by two of the leading researchers in the field.
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Addresses the learning strategies by describing the models of a number of different algorithms used in machine learning. Every chapter addresses a different learning algorithm and contains detailed diagrams and real-world applications.
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Offers a unique perspective on machine learning by presenting it from a probabilistic standpoint. It covers a wide range of topics from Bayesian inference to Gaussian processes.
Covers the topic of sparsity in machine learning. Sparsity property of data that has many zeros. This book shows how to use sparsity to improve the performance of machine learning algorithms.
Provides a comprehensive overview of statistical learning, a subfield of machine learning that deals with supervised learning. It is written by three of the leading researchers in the field.
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Presents a unified view of machine learning from a Bayesian and optimization perspective. It covers a wide range of topics from supervised learning to reinforcement learning.
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Provides a comprehensive overview of machine learning algorithms, covering a wide range of topics from linear regression to neural networks. It is written in a clear and concise style, making it accessible to readers with a variety of backgrounds.
Covers the topic of machine learning for data streams. Data streams are continuous flows of data that are too large to be stored in memory. This book shows how to use machine learning algorithms to process data streams in real time.
Covers the topic of machine learning for text data. Text data special type of data that has unique characteristics. This book shows how to use machine learning algorithms to process text data.
Covers the topic of machine learning for audio, image and video analysis. This book shows how to use machine learning algorithms to process audio, images, and videos.
Explores the intersection of computer science and human decision-making. The authors provide insights into how algorithms can be used to improve our decision-making processes.
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Explores the paradox of choice, arguing that having too many options can lead to indecision and regret. Iyengar provides practical advice on how to navigate choices effectively and make better decisions.
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Presents a collection of 50 decision-making models that can be applied to a wide range of situations. Mauboussin provides clear and concise explanations of each model, along with real-world examples of how they can be used.
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Explores the science of habit formation and how we can use it to change our behavior. Duhigg provides practical advice on how to break bad habits and create good ones, making it relevant for personal decision-making and self-improvement.
Provides a framework for evaluating the quality of strategies. Rumelt argues that good strategies are clear, focused, and aligned with an organization's capabilities.
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Draws on the author's experience as a professional poker player to provide insights into how to make better decisions under uncertainty. Duke emphasizes the importance of probabilistic thinking and risk management.
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Provides guidance on how to build trust and credibility as a professional. It covers topics such as communication, active listening, and problem-solving, which are essential skills for effective decision-making.
Explores the relationship between choice and happiness. Schwartz argues that having too many choices can lead to anxiety, paralysis, and dissatisfaction.
Examines the science of willpower and provides practical strategies for developing self-control. McGonigal explores the role of willpower in decision-making and how it can be strengthened.
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