Machine Learning Series
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Machine Learning With Pytorch And Scikit Learn
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25
Machine Learning With Pytorch And Scikit Learn 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 2022-02-25 with Computers categories.
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Deep Learning For Time Series Forecasting
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2018-08-30
Deep Learning For Time Series Forecasting written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Computers categories.
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
Machine Learning Using R
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Author : Karthik Ramasubramanian
language : en
Publisher: Apress
Release Date : 2019-01-04
Machine Learning Using R written by Karthik Ramasubramanian and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-04 with Computers categories.
Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with 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 balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. What You'll Learn Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R Who This Book is For Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.
Machine Learning
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Author : Andrew Park
language : en
Publisher:
Release Date : 2020-01-21
Machine Learning written by Andrew Park and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-21 with categories.
Master the world of Machine Learning and Data Science with this comprehensive 2-in-1 bundle. If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily, then keep reading. Data Science and Machine Learning are the biggest buzzwords in the business world nowadays. Many businesses know the importance of collecting information, but as they can collect so much data in a short period, the real question is: "what is the next step?" Data Science includes all the different procedures that must be implemented when working with data: collecting and cleaning them, analyzing them, applying Machine Learning algorithms and models, and then presenting your findings from the analysis with some good data visualizations. Machines and automation represent a huge part of our daily life. They are becoming part of our experience, and existence. Artificial Intelligence is currently one of the most thriving fields any programmer would wish to delve into, and for a good reason: this is the future! Simply put, Machine Learning is about teaching machines to think and make decisions as we would. The difference between the way machines learn and the way we do is that while for the most part we learn from experiences, machines learn from data. In book one, PYTHON MACHINE LEARNING, you will learn: What is Machine Learning and how it is applied in real-world situations Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence Machine learning training models, Regression techniques and Linear Regression in Python How to use Lists and Modules in Python The 12 essential libraries for Machine Learning in Python Artificial Neural Networks And Much More! In book two, PYTHON DATA SCIENCE, you will learn: What Data Science is all about and why so many companies are using it to give them a competitive edge. Why Python and how to use it to implement Data Science The main Data Structures & Object-Oriented Programming, Functions and Modules in Python with practical codes and exercises The 7 most important algorithms and models in Data Science Data Aggregation, Group Operations, Databases and Data in the Cloud 9 important Data Mining techniques in Data Science And So Much More! Where most books only focus on how collecting and cleaning the data, this book goes further, providing guidance on how to perform a proper analysis in order to extract precious information that may be vital for a business. Don't miss the opportunity to master the key points of Machine Learning technology and understand how researchers are breaking the boundaries of Data Science to mimic human intelligence in machines. Even if some concepts of Machine Learning algorithms can appear complex to most computer programming beginners, this book takes the time to explain them in a simple and concise way. Understanding Machine Learning and Data Science is easier than it looks. You just need the right guidance. And this book provides all the knowledge you need in a simple and practical way. Regardless of your previous experience, you will learn, the techniques to manipulate and process datasets, the principles of Python programming, and its most important real-world applications. Would You Like To Know More?Scroll Up and Click on the BUY NOW Button to Get Your Copy!
Data Science And Machine Learning Series Deep Learning Facts Frameworks And Functionality
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Author : Zacharias Voulgaris
language : en
Publisher:
Release Date : 2018
Data Science And Machine Learning Series Deep Learning Facts Frameworks And Functionality written by Zacharias Voulgaris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Explore deep learning and how it applies to data science and compares to traditional machine learning. See how deep learning works in terms of both architecture and design, and learn about different frameworks including Apache MXNet, PyTorch, and TensorFlow. Related concepts are covered including Artificial Neural Networks (ANNs), Multi-Layer Perceptrons (MLPs), and Natural Language Processing (NLP). Programming languages including Python and Julia are discussed. Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos.
Machine Learning
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Author : Ivan Bratko
language : en
Publisher:
Release Date : 1999
Machine Learning written by Ivan Bratko and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Machine learning categories.
Data Science And Machine Learning With Python
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Author : Swapnil Saurav
language : en
Publisher:
Release Date : 2020-12-10
Data Science And Machine Learning With Python written by Swapnil Saurav and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-10 with categories.
Unlock your potential as an AI and ML professional! This book covers basic to advanced level topics required to master the Machine Learning concepts. There are lot of programs implemented which goes with the explaination - thats why we call it Learn and Practice. Book uses Scikit-learn (formerly scikits.learn and also known as sklearn) is the most popular package and also a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.Happy Coding in Python
Data Science And Machine Learning Series
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Author : Zacharias Voulgaris
language : en
Publisher:
Release Date : 2018
Data Science And Machine Learning Series written by Zacharias Voulgaris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Learn all about time-series analysis, which can be leveraged within many industries including economics and artificial intelligence (AI). Explore the different kinds of time-series analysis, including stats-based, machine learning-based, and AI-based. Understand the most common evaluation metrics used, and the evaluation packages that are available within Python and Julia. Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos.
Machine Learning Revised And Updated Edition
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Author : Ethem Alpaydin
language : en
Publisher: MIT Press
Release Date : 2021-08-17
Machine Learning Revised And Updated Edition written by Ethem Alpaydin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-17 with Computers categories.
MIT presents a concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars. No in-depth knowledge of math or programming required! Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don’t yet use every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin explains that as Big Data has grown, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He covers: • The evolution of machine learning • Important learning algorithms and example applications • Using machine learning algorithms for pattern recognition • Artificial neural networks inspired by the human brain • Algorithms that learn associations between instances • Reinforcement learning • Transparency, explainability, and fairness in machine learning • The ethical and legal implicates of data-based decision making A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming—making it accessible for everyday readers and easily adoptable for classroom syllabi.
Data Science And Machine Learning Series
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Author : Zacharias Voulgaris
language : en
Publisher:
Release Date : 2018
Data Science And Machine Learning Series written by Zacharias Voulgaris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Learn all about the Extreme Learning Machine (ELM) Artificial Neural Network (ANN), and how you can best leverage it within Artificial Intelligence (AI) and data science projects including within regression, alternate feature generation, and classification. We cover the ELM architecture along with the ELM packages available in Julia and Python. We'll conclude by comparing ELMs to multilayer perceptrons (MLPs). Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos.