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DescriptionPython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step;
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updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can;
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. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used;
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TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine;
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Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by;
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Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine;
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case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will Learn Discover applied;
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learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions;
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working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark * Comprehensive practical;
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learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions;
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Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB (R), this textbook;
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This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on;
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The new edition of an introduction to the art of computational problem solving using Python. This book introduces students with little;
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you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python;
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the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and;
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, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also;
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Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data;
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learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions;
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is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python;
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is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python;
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Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This;
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The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for;
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Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as;
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Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs;
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