Contemporary Machine Learning Methods Harnessing Scikit Learn And Tensorflow
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Contemporary Machine Learning Methods Harnessing Scikit Learn And Tensorflow
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-03
Contemporary Machine Learning Methods Harnessing Scikit Learn And Tensorflow written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
"Contemporary Machine Learning Methods: Harnessing Scikit-Learn and TensorFlow" is an indispensable resource for data scientists and machine learning practitioners eager to sharpen their skills and stay at the forefront of technology. This book offers a comprehensive exploration of modern machine learning methodologies, encompassing innovative regression and classification techniques, along with complex neural network architectures using TensorFlow. Explore practical implementations and real-world examples that demystify intricate concepts like unsupervised learning, deep learning optimizations, natural language processing, and feature engineering with clarity. Each chapter serves as a step-by-step guide to applying these contemporary methods, complete with code samples and thorough explanations. Whether you're a professional aiming to deploy machine learning solutions at an enterprise level, an academic researcher investigating computational innovations, or a postgraduate student interested in cutting-edge AI, this book equips you with the insights, tools, and expertise needed to effectively leverage machine learning technologies. Master the nuances of machine learning with "Contemporary Machine Learning Methods: Harnessing Scikit-Learn and TensorFlow" and convert data into impactful knowledge.
Python Machine Learning
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-09-20
Python Machine Learning written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-20 with Computers categories.
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.
Hands On Machine Learning With Scikit Learn And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-03-13
Hands On Machine Learning With Scikit Learn And Tensorflow written by Aurélien Géron and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-13 with Computers categories.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Python Machine Learning
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Author : Sebastian Raschka
language : en
Publisher:
Release Date : 2017
Python Machine Learning written by Sebastian Raschka and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Python (Computer program language) categories.
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from s ...
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-05
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-05 with Computers categories.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Tensorflow Machine Learning
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Author : Benjamin Smith
language : en
Publisher:
Release Date : 2021-01-04
Tensorflow Machine Learning written by Benjamin Smith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-04 with categories.
Have you ever wondered how machine learning works? These days, machine learning, deep learning and neural nets are common terms and they are here to stay as a part of our everyday language. Machine learning is not the easiest of topics to teach, purely because there is so much to it. Machine learning, deep learning and artificial intelligence are used in more applications than most humans even think about - email, Amazon, Netflix, Spotify, and other popular online marketplaces use machine learning to weed out spam emails and bring you recommendations based on your shopping or streaming preferences. Machine learning is used in healthcare, in finance, in just about every industry you can think of - it's here to stay, whether we like it or not.One of the most important parts of learning machine learning is knowing which algorithm to choose and which library. Python is the most popular machine learning programming language and it has a huge advantage over other languages - the large amount of built-in libraries; three of the most important are TensorFlow, Keras and Scikit-Learn. And that's what this book is about - machine learning with TensorFlow, Keras and Scikit-learn. Here's what you will learn: -What machine learning is-How it applies in the real world-Different models and learning types-Different machine learning algorithms-Deep learning vs. machine learning-What TensorFlow is and how to use it-What TensorFlow comprises-Operators, variables, placeholders, and more-What Keras is and how to use it-Keras vs. TensorFlow-How to use Keras for linear regression-How to use Keras to build a neural net-What Scikit-Learn is and how to use it-Using Scikit-Learn to build regression and classification trees-How to build a random forest model-How to install Keras, TensorFlow and Scikit-LearnAnd much more!All the practical examples in the book use Python, so you are expected to need some knowledge of the language before you start. If you're looking to advanced your skills in machine learning, then this is the book for you! Grab your copy of this book today!
Advanced Machine Learning Techniques
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Author : Adam King
language : en
Publisher: Independently Published
Release Date : 2024-04-21
Advanced Machine Learning Techniques written by Adam King and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-21 with Computers categories.
"Advanced Machine Learning Techniques with Scikit-Learn and TensorFlow" is an essential guide for data scientists and machine learning practitioners looking to elevate their skills to the next level. Packed with cutting-edge techniques, this book provides an expert overview of advanced machine learning methodologies, starting from sophisticated regression and classification strategies to intricate neural network architectures with TensorFlow. Delve into practical implementations and real-world examples that explain complex concepts such as unsupervised learning, deep learning optimizations, natural language processing, and feature engineering with precision. Each chapter presents a clear, actionable guide to implementing these advanced techniques, supported by code examples and detailed explanations. Whether you're a professional looking to implement machine learning solutions at scale, an academic researcher exploring computational advancements, or a postgraduate student keen on the latest in AI, this book offers the tools, insights, and expertise necessary to harness the power of machine learning with efficiency and accuracy. Master the art and science of machine learning with "Advanced Machine Learning Techniques with Scikit-Learn and TensorFlow" and transform data into actionable knowledge.
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-10-04
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-04 with Computers categories.
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Machine Learning Demystified A Practical Guide To Building Smarter Systems
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Author : Guillaume Lessard
language : en
Publisher: iD01t Productions
Release Date : 2025-01-14
Machine Learning Demystified A Practical Guide To Building Smarter Systems written by Guillaume Lessard and has been published by iD01t Productions this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-14 with Computers categories.
Machine Learning Demystified: A Practical Guide to Building Smarter Systems Unlock the power of Artificial Intelligence with Machine Learning Demystified, a comprehensive and beginner-friendly guide to understanding and applying machine learning principles in real-world scenarios. Authored by Guillaume Lessard, this book bridges the gap between complex algorithms and practical applications, making AI accessible to everyone—from curious beginners to seasoned professionals. What Awaits Inside? Simplified Learning: Master the fundamentals of machine learning with relatable examples and step-by-step tutorials. Algorithm Essentials: Explore supervised and unsupervised learning, neural networks, natural language processing, and computer vision. Hands-On Projects: Dive into real-world applications like spam detection, housing price prediction, and image recognition systems. Practical Tools: Gain expertise in industry-standard frameworks such as TensorFlow, PyTorch, and Scikit-Learn. Ethical AI Practices: Learn to build responsible AI systems with fairness, transparency, and privacy in mind. Why Choose This Book? Many AI resources overwhelm readers with heavy mathematics and jargon. This guide adopts a unique approach, focusing on practical, actionable insights that empower you to create smarter systems. Whether you’re exploring AI's potential for business or personal projects, this book provides the clarity and confidence needed to succeed. Who Is It For? Aspiring data scientists and developers Business professionals exploring AI-driven solutions Anyone passionate about the future of technology Embark on a journey to demystify machine learning and turn your ideas into reality. Your smarter systems start here!
Machine Learning Using Tensorflow Cookbook
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Author : Alexia Audevart
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-08
Machine Learning Using Tensorflow Cookbook written by Alexia Audevart and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-08 with Mathematics categories.
Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more Key FeaturesDeep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced techniques to make your algorithms faster and more accurateBook Description The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios. What you will learnTake TensorFlow into productionImplement and fine-tune Transformer models for various NLP tasksApply reinforcement learning algorithms using the TF-Agents frameworkUnderstand linear regression techniques and use Estimators to train linear modelsExecute neural networks and improve predictions on tabular dataMaster convolutional neural networks and recurrent neural networks through practical recipesWho this book is for If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.