Download PDF Book Of Practical Time Series Analysis: Prediction with Statistics and Machine Learning By Aileen Nielsen:

Download PDF Book Of Practical Time Series Analysis: Prediction with Statistics and Machine Learning By Aileen Nielsen

Download PDF Book Of Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python By David Paper

Download PDF Book Of Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python By David Paper:

"Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python" by David Paper is a practical guide to using Scikit-Learn for machine learning applications. The book is aimed at data scientists and machine learning practitioners who want to learn how to use Scikit-Learn for their data analysis projects.

The book covers various topics related to using Scikit-Learn for machine learning, including data preprocessing, model selection, model evaluation, and hyperparameter tuning. The author provides practical examples and case studies throughout the book to illustrate the concepts being discussed.

One of the strengths of the book is that it provides a hands-on approach to learning Scikit-Learn. The author provides clear and concise explanations of the concepts being discussed and then provides code examples to illustrate how to apply these concepts in practice.

The book also covers important topics such as feature selection, data imputation, and text classification. The author provides clear explanations of these topics, making it easy for readers to understand how to apply these techniques to their own data analysis projects.

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Download PDF Book Of Deep Learning with Structured Data By Mark Ryan

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"Deep Learning with Structured Data" by Mark Ryan is a comprehensive guide to using deep learning techniques with structured data. The book is aimed at data scientists and machine learning practitioners who want to develop a deep understanding of deep learning for structured data.

The book covers various topics related to deep learning for structured data, including data preprocessing, feature engineering, deep learning models such as neural networks, and model evaluation. The author provides practical examples and case studies throughout the book to illustrate the concepts being discussed.

One of the strengths of the book is that it provides a clear and concise explanation of how to apply deep learning techniques to structured data. The author explains complex concepts in simple terms, making it easy for readers to understand the underlying principles of deep learning for structured data.

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Download PDF Book Of Data Science from Scratch: First Principles with Python By Joel Grus

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"Data Science from Scratch: First Principles with Python" by Joel Grus is a comprehensive guide to data science for beginners. The book is aimed at individuals with little or no experience in programming or data analysis.

The book covers various topics related to data science, including Python programming, statistics, machine learning, and data visualization. The author provides practical examples and case studies throughout the book to illustrate the concepts being discussed.

One of the strengths of the book is that it provides a clear and concise explanation of the fundamentals of data science. The author explains complex concepts in simple terms, making it easy for readers to understand the underlying principles of data science.

The book also covers important topics such as data cleaning, data wrangling, and data analysis. The author provides clear explanations of these topics, making it easy for readers to understand how to apply these techniques in practice.

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Download PDF Book Of Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists By Alice Zheng, Amanda Casari

Download PDF Book Of Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists By Alice Zheng, Amanda Casari:

"Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists" by Alice Zheng and Amanda Casari is a comprehensive guide to the principles and techniques of feature engineering for machine learning. The book is aimed at data scientists and machine learning practitioners who want to develop a deep understanding of feature engineering.

The book covers various topics related to feature engineering, including data cleaning, feature selection, feature transformation, and feature creation. The authors provide practical examples and case studies throughout the book to illustrate the concepts being discussed.

One of the strengths of the book is that it provides a clear and concise explanation of the importance of feature engineering in machine learning. The authors explain how feature engineering can impact the performance of a machine learning model and provide practical guidance on how to develop effective feature engineering strategies.

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Download PDF Book Of Deep Learning Cookbook: Practical Recipes to Get Started Quickly By Douwe Osinga

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"Deep Learning Cookbook: Practical Recipes to Get Started Quickly" by Douwe Osinga is a cookbook-style guide that provides practical recipes for implementing deep learning techniques. The book is aimed at individuals with a basic understanding of machine learning and Python programming.

The book is organized into chapters that cover different aspects of deep learning, including feedforward neural networks, convolutional neural networks, recurrent neural networks, and reinforcement learning. Each chapter is divided into recipes, which are self-contained code examples that can be easily adapted to different use cases.

One of the strengths of the book is that it provides a wide range of practical recipes that cover many different aspects of deep learning. The recipes are well-organized and easy to follow, making it easy for readers to get started with deep learning quickly. The code examples are provided in Python, which is a widely used language in the field of machine learning.

The book also covers important topics such as data preprocessing, model evaluation, and hyperparameter tuning. The author provides clear and concise explanations of these topics, making it easy for readers to understand the underlying concepts.

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Download PDF Book Of Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks By Umberto Michelucci

Download PDF Book Of Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks By Umberto Michelucci

"Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks" by Umberto Michelucci is a comprehensive guide to understanding the fundamental principles of deep learning and the practical applications of deep neural networks. The book is geared towards individuals with a solid understanding of linear algebra, calculus, and probability theory.

The book provides a case-based approach, where the reader can learn by working through practical examples and case studies. The book covers various topics such as feedforward neural networks, convolutional neural networks, recurrent neural networks, autoencoders, and generative models. Each chapter is accompanied by Python code examples that illustrate the concepts being discussed.

One of the strengths of the book is that it provides a good balance between theory and practice. The author does a good job of explaining the underlying mathematical concepts and provides clear and concise explanations of how they are applied in practice. The book also covers important topics such as hyperparameter tuning, regularization, and optimization techniques.

Overall, "Applied Deep Learning" is an excellent resource for individuals looking to develop a solid understanding of deep learning and its practical applications. The book is well-written, easy to follow, and provides plenty of practical examples that can be easily adapted to a wide range of applications.