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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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examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. What You;
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implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. What You Will Learn Understanding the end;
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Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil;
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success. This guide is meant to help you with understanding the fundamentals of Machine Learning with Python, as well as making predictions and;
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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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Python Machine Learning for BeginnersMachine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a;
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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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reinforcement learning Who this book is forBuilding Machine Learning Systems with Python is for data scientists, machine learning developers, and;
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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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techniques of data analysis * Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods * Learn how;
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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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learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions;
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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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This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated;
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This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated;
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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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, 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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Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning;
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Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It;
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, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive;
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Understand the concept of supervised learning and its applications Implement common supervised learning algorithms using machine learning Python;
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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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and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives;
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