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| Machine Learning For Dummies by John Paul Mueller , Luca Massaron PDF Book Free Download |
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Machine Learning For Dummies by John Paul Mueller , Luca Massaron Book Details
| Book Name | Machine Learning For Dummies |
| Author | John Paul Mueller , Luca Massaron |
| Category | Mechanical Engineering Books, Education Books |
| Book Language | English |
| Pages | 435 |
| ISBN | 9781119245513 |
| Country | India |
| Book Size | 8 MB |
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About Machine Learning For Dummies by John Paul Mueller , Luca Massaron Book
Machine Learning For Dummies is a popular introductory book that provides a beginner-friendly overview of the field of machine learning. The "For Dummies" series is known for its accessible and easy-to-understand explanations of complex topics, making them suitable for individuals with little to no prior knowledge of the subject matter. The book "Machine Learning For Dummies" aims to demystify the concepts of machine learning and provide readers with a foundational understanding of its key principles.
The book typically covers topics such as:
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Introduction to Machine Learning: An overview of what machine learning is, its applications, and its relevance in today's world.
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Types of Machine Learning: Exploring different categories of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
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Data Preprocessing: Understanding the importance of data preparation, cleaning, and feature engineering before training machine learning models.
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Machine Learning Algorithms: Introducing various machine learning algorithms, such as decision trees, support vector machines, neural networks, and more.
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Model Training and Evaluation: How to train machine learning models using training data and assess their performance using testing or validation data.
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Overfitting and Underfitting: Learning about the concepts of overfitting (models too complex) and underfitting (models too simple) and how to strike the right balance.
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Feature Selection and Dimensionality Reduction: Techniques for selecting relevant features and reducing the dimensionality of data to improve model performance.
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Model Interpretability: Understanding how to interpret and explain the decisions made by machine learning models.
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Ensemble Learning: Exploring the concept of combining multiple models to improve prediction accuracy and robustness.
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Ethical Considerations: Discussing the ethical implications of machine learning, including bias, fairness, and privacy.
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Practical Examples: Providing hands-on examples and case studies to help readers apply machine learning concepts to real-world problems.
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Future Trends: Highlighting emerging trends and advancements in the field of machine learning.
It's important to note that while "Machine Learning For Dummies" provides a beginner-friendly introduction to the field, machine learning is a vast and rapidly evolving field. As such, the book might not cover the very latest developments or highly specialized topics in depth. However, it serves as an excellent starting point for individuals who want to grasp the basics of machine learning before diving deeper into more advanced resources and courses.
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