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Machine Learning - A Complete Exploration of Highly Advanced Machine Learning Concepts, Best Practices and Techniques

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Machine Learning - A Comprehensive, Step-by-Step Guide to Learning and Applying Advanced Concepts and Techniques in Machine Learning

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Machine Learning for Biometrics

, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this;

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Mastering Azure Machine Learning

Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using;

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Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us

AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this;

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Mastering Machine Learning for Penetration Testing

generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for;

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Machine Learning for Finance

emphasis on financial applications Deep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement;

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Practical Machine Learning for Data Analysis Using Python

to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful;

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Python

-of-the-art large-scale machine learning techniques * Learn to use Python code to implement a range of machine learning algorithms and techniques;

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Machine Learning Foundations

techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides;

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Machine Learning with Scala Quick Start Guide

techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a;

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Machine Learning for Developers

techniques and how to use them to solve real problems * Get a feel for advanced concepts, using popular programming frameworks. * Prepare yourself;

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Machine Learning for Healthcare

of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning;

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Practical Machine Learning with Python

of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach;

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Practical Machine Learning

most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into;

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R Machine Learning By Example

Learn * Utilize the power of R to handle data extraction, manipulation, and exploration techniques * Use R to visualize data spread across;

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Data Mining and Machine Learning in Cybersecurity

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role;

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Machine Learning with R Cookbook

of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if;

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Mastering Java Machine Learning

tools in a wide range of techniques, with code and practical usage. * More than 10 real-world case studies in machine learning highlighting;

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Mastering OpenCV 4 with Python

Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web;

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Welding and Cutting Case Studies with Supervised Machine Learning

This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and;

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Welding and Cutting Case Studies with Supervised Machine Learning

This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and;

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Hands-On Machine Learning with IBM Watson

Run supervised and unsupervised techniques in the cloud Understand how to create a Spark pipeline in Watson Studio Implement deep learning;

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TensorFlow 2.0 Quick Start Guide

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train;

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Introduction to Statistical Machine Learning

approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics;

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Python Machine Learning By Example

machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven;

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Python Machine Learning

updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book;

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