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Ideal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter;
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where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the;
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Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a;
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Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine;
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, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from;
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application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras. By the end of this book;
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Machine Learning What are the Python Keywords? How to make predictions using;
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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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machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic;
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Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins;
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Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins;
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Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries;
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Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully;
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machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine;
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decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous;
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Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using Python Follow a deep learning;
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techniques of data analysis * Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods * Learn how;
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Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About This Book;
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clustering using the k-means and mean shift algorithms Understand the fundamentals of deep learning via practical examples Who this book;
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Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda;
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using various interactive and dynamic Python libraries How to implement models in Streamlit web applications Who This Book Is ForProfessionals;
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Deep Learning Using Python and TensorFlow includes valuable case studies across professional domains, including banking, insurance, e-commerce;
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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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your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced;
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If you've been using Python for some time and want to become even better at it, then Python Machine Learning is the first book you should;
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