Practical Machine Learning For Streaming Data With Python
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Practical Machine Learning For Streaming Data With Python
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Author : Sayan Putatunda
language : en
Publisher:
Release Date : 2021
Practical Machine Learning For Streaming Data With Python written by Sayan Putatunda and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow. Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more. You will: Understand machine learning with streaming data concepts Review incremental and online learning Develop models for detecting concept drift Explore techniques for classification, regression, and ensemble learning in streaming data contexts Apply best practices for debugging and validating machine learning models in streaming data context Get introduced to other open-source frameworks for handling streaming data.
Machine Learning For Streaming Data With Python
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Author : Joos Korstanje
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-07-15
Machine Learning For Streaming Data With Python written by Joos Korstanje 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 2022-07-15 with Computers categories.
Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming Key Features • Work on streaming use cases that are not taught in most data science courses • Gain experience with state-of-the-art tools for streaming data • Mitigate various challenges while handling streaming data Book Description Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models. What you will learn • Understand the challenges and advantages of working with streaming data • Develop real-time insights from streaming data • Understand the implementation of streaming data with various use cases to boost your knowledge • Develop a PCA alternative that can work on real-time data • Explore best practices for handling streaming data that you absolutely need to remember • Develop an API for real-time machine learning inference Who this book is for This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.
Practical Machine Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2017-12-20
Practical Machine Learning With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-20 with Computers categories.
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
Practical Machine Learning For Data Analysis Using Python
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Author : Abdulhamit Subasi
language : en
Publisher: Academic Press
Release Date : 2020-06-05
Practical Machine Learning For Data Analysis Using Python written by Abdulhamit Subasi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-05 with Computers categories.
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
Python Machine Learning
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Author : Andrew Park
language : en
Publisher: Andrew Park
Release Date : 2021-04-27
Python Machine Learning written by Andrew Park and has been published by Andrew Park this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with categories.
★ 55% OFF for Bookstores! NOW at $ 13.49 instead of $ 29.97! LAST DAYS! ★ Do you want to learn how to design and master different Machine Learning algorithms quickly and easily?Your Customers Will Love This Amazing Guide! Today, we live in the era of Artificial Intelligence. Self-driving cars, customized product recommendations, real-time pricing, speech and facial recognition are just a few examples proving this truth. Also, think about medical diagnostics or automation of mundane and repetitive labor tasks; all these highlight the fact that we live in interesting times. From research topics to projects and applications in different stages of production, there is a lot going on in the world of Machine Learning. Machines and automation represent a huge part of our daily life. They are becoming part of our experience and existence. This is Machine Learning. 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. Starting from scratch, Python Machine Learning explains how this happens, how machines build their experience and compounding knowledge. Data forms the core of Machine Learning because within data lie truths whose depths exceed our imagination. The computations machines can perform on data are incredible, beyond anything a human brain could do. Once we introduce data to a machine learning model, we must create an environment where we update the data stream frequently. This builds the machine's learning ability. The more data Machine Learning models are exposed to, the easier it is for these models to expand their potential. Some of the topics that we will discuss inside include: What is Machine Learning and how it is applied in real-world situations Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence Supervised learning, unsupervised learning, and semi-supervised learning The place of Regression techniques in Machine Learning, including Linear Regression in Python Machine learning training models How to use Lists and Modules in Python The 12 essential libraries for Machine Learning in Python What is the Tensorflow library Artificial Neural Networks And Much More! While most books only focus on widespread details without going deeper into the different models and techniques, Python Machine Learning explains how to master the concepts of Machine Learning technology and helps you to understand how researchers are breaking the boundaries of Data Science to mimic human intelligence in machines using various Machine Learning algorithms. 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. Would You Like To Know More? Buy It NOW And Let Your Customers Get Addicted To This Amazing Book!
Machine Learning With Python
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Author : Oliver Theobald
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-06
Machine Learning With Python written by Oliver Theobald 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 2024-03-06 with Computers categories.
Unlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlessly Key Features Navigate through Python's machine learning libraries effectively Learn exploratory data analysis and data scrubbing techniques Design and evaluate machine learning models with precision Book DescriptionThe course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.What you will learn Analyze datasets for insights Scrub data for model readiness Understand key ML algorithms Design and validate models Apply Linear and Logistic Regression Utilize K-Nearest Neighbors and SVMs Who this book is for This course is ideal for aspiring data scientists and professionals looking to integrate machine learning into their workflows. A basic understanding of Python and statistics is beneficial.
Modern Data Architectures With Python
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Author : Brian Lipp
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-09-29
Modern Data Architectures With Python written by Brian Lipp 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 2023-09-29 with Computers categories.
Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features Develop modern data skills used in emerging technologies Learn pragmatic design methodologies such as Data Mesh and data lakehouses Gain a deeper understanding of data governance Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market. By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What you will learn Understand data patterns including delta architecture Discover how to increase performance with Spark internals Find out how to design critical data diagrams Explore MLOps with tools such as AutoML and MLflow Get to grips with building data products in a data mesh Discover data governance and build confidence in your data Introduce data visualizations and dashboards into your data practice Who this book is forThis book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.
Python Real World Machine Learning
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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-11-14
Python Real World Machine Learning written by Prateek Joshi 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 2016-11-14 with Computers categories.
Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Python Machine Learning Cookbook by Prateek Joshi Advanced Machine Learning with Python by John Hearty Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and approach This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!
Adaptive Machine Learning Algorithms With Python
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Author : Chanchal Chatterjee
language : en
Publisher:
Release Date : 2022
Adaptive Machine Learning Algorithms With Python written by Chanchal Chatterjee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment. You will: Apply adaptive algorithms to practical applications and examples Understand the relevant data representation features and computational models for time-varying multi-dimensional data Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data Speed up your algorithms and put them to use on real-world stationary and non-stationary data Master the applications of adaptive algorithms on critical edge device computation applications.
Hands On Deep Learning Architectures With Python
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30
Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu 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 2019-04-30 with Computers categories.
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book