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Are you a machine learning enthusiast looking for a practical day to day application? Or are you just trying to incorporate machine;
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Are you someone who is interested in how the next generation of machines can help you? Machine Learning and AI are more than just science;
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, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding;
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solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML;
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a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid;
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learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and;
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robust statistical models, built for a range of datasets Who This Book Is For If you are a C# or an F# developer who now wants to explore;
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shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with;
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The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine;
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with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model;
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powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you have a basic knowledge of machine learning and;
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you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.;
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challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are;
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you will learn Discover key Java machine learning libraries Implement concepts such as classification, regression, and clustering Develop a;
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learning Who this book is forIf you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine;
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regular IDE. You'll start by learning how to use Jupyter Notebooks to improve the way you program with Python. After getting a good grounding;
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Who this book is forIf you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine;
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the journey unfolds In Detail Data science and machine learning are some of the top buzzwords in the technical world today. A resurging;
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algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you;
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. By the end of this book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound;
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, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are;
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ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine;
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models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book;
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challenges Book DescriptionThis eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh;
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to build a machine learning model that will answer your questions within a blink of your eyes? If you responded yes to any of the above;
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to build a machine learning model that will answer your questions within a blink of your eyes? If you responded yes to any of the above;
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