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Machine Learning Techniques for Text. Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

Nikos Tsourakis

With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code.A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions.By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.

Machine Learning with Apache Spark Quick Start Guide. Uncover patterns, derive actionable insights, and learn from big data using MLlib

Jillur Quddus

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.

Machine Learning with R Cookbook. Analyze data and build predictive models - Second Edition

AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.

Machine Learning with R. Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data - Fourth Edition

Brett Lantz

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data.You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data.

Machine Learning with R Quick Start Guide. A beginner's guide to implementing machine learning techniques from scratch using R 3.5

Iván Pastor Sanz

Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline.From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling.By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.

Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python

Kevin Jolly

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.

Machine Learning with Spark. Develop intelligent, distributed machine learning systems - Second Edition

Rajdeep Dua, Manpreet Singh Ghotra

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.

Machine Learning with TensorFlow 1.x. Second generation machine learning with Google's brainchild - TensorFlow 1.x

Saif Ahmed, Quan Hua, Shams Ul Azeem

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics

Rich Collier, Bahaaldine Azarmi

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.

Managing Data as a Product. Design and build data-product-centered socio-technical architectures

Andrea Gioia, Giulio Scotti

Traditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets.Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you’ll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. It also covers data modeling in distributed environments and its role in enabling modern generative AI.By the end of this book, you’ll understand product-centric data architecture and how to adopt it.*Email sign-up and proof of purchase required

Managing Data Integrity for Finance. Discover practical data quality management strategies for finance analysts and data professionals

Jane Sarah Lat

Data integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations.The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases.After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.

Managing Data Science. Effective strategies to manage data science projects and build a sustainable team

Kirill Dubovikov

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.

Managing Microsoft Teams: MS-700 Exam Guide. Configure and manage Microsoft Teams workloads and achieve Microsoft 365 certification with ease

Peter Rising, Nate Chamberlain

Do you want to build and test your proficiency in the deployment, management, and monitoring of Microsoft Teams features within the Microsoft 365 platform? Managing Microsoft Teams: MS-700 Exam Guide will help you to effectively plan and implement Microsoft Teams using the Microsoft 365 Teams admin center and Windows PowerShell. You’ll also discover best practices for rolling out and managing MS services for Teams users within your Microsoft 365 tenant. The chapters are divided into three easy-to-follow parts: planning and design, feature policies and administration, and team management, while aligning with the official MS-700 exam objectives to help you prepare effectively for the exam.The book starts by taking you through planning and design, where you’ll learn how to plan migrations, make assessments for network readiness, and plan and implement governance tasks such as configuring guest access and monitoring usage. Later, you’ll understand feature administration, focusing on collaboration, meetings, live events, phone numbers, and the phone system, along with applicable policy configurations. Finally, the book shows you how to manage Teams and membership settings and create app policies.By the end of this book, you'll have learned everything you need to pass the MS-700 certification exam and have a handy reference guide for MS Teams.

Market Research and Analysis. Mastering Market Research: Advanced Methods, Design, and Data Analysis

Mercury Learning and Information, Marcus Goncalves

This book offers an in-depth exploration of market research and analysis, guiding readers through the entire process from defining research objectives to communicating results. Begin by understanding the purpose and ethics of market research, laying a strong groundwork for your studies. Progress to defining precise research objectives and exploring secondary research methods to gather existing information.Next, engage with primary research methods, focusing on both quantitative and qualitative approaches. Learn how to develop and distribute surveys, choose the right sampling techniques, and utilize tools for data mining and web scraping. Gain insights into focus groups and observation studies, understanding how these qualitative methods can provide depth to your research.Finally, master the art of data analysis and result communication. Explore descriptive statistics, hypothesis testing, and inferential statistics to make sense of your data. Learn to effectively present your findings to stakeholders, ensuring your research translates into actionable insights. By the end of the course, you will be well-equipped to conduct thorough market research and communicate your results effectively.

Marketing i analityka biznesowa dla początkujących. Poznaj najważniejsze narzędzia i wykorzystaj ich możliwości

Kinga Sroka

Witaj w świecie fascynujących danych! Biznes nie istnieje bez twardych danych, założeń, KPI i ich realizacji. Także ta jego część, która jest związana z promocją. Szczególnie online. Dziś nie można być marketerem z prawdziwego zdarzenia i nie znać chociażby narzędzi oferowanych przez Google ― Analytics, Tag Manager, Search Console i Trends. Te nazwy kojarzą Ci się z czymś niezrozumiałym albo budzą obawy? Nie martw się i sięgnij po tę książkę! Dowiesz się z niej, jak efektywnie korzystać z internetowych rozwiązań analitycznych. Ten poradnik stanowi świetne wprowadzenie do marketingu i analityki biznesowej online dla osób, które dopiero zapoznają się z tym tematem. To nie tylko przegląd narzędzi współczesnego analityka. Autorka opisuje również kompetencje, które będą potrzebne osobom z branży w najbliższej przyszłości, wskazuje miejsca, gdzie już można je zdobywać, wreszcie podpowiada, jakie umiejętności trzeba mieć, by otrzymać wymarzoną pracę w firmach zajmujących się danymi cyfrowymi.

Master Your Data with Power Query in Excel and Power BI. Leveraging Power Query to Get & Transform Your Task Flow

MrExcel's Holy Macro! Books, Miguel Escobar, Ken...

This book equips you with the essential skills to master Power Query in Excel and Power BI. Starting with the basics, you'll learn query management, data types, and error handling, establishing a solid foundation. You'll explore techniques to move queries between Excel and Power BI, ensuring seamless workflow integration. As the guide progresses, you'll delve into data import methods from flat files, Excel, web-based, and relational sources, while performing key transformations like appending, combining, and reshaping data.Advanced topics such as conditional logic, Power Query values, and M Language fundamentals will enhance your ability to customize and optimize queries. The book also covers the creation of parameters and custom functions, alongside applying sophisticated date and time techniques.Finally, you'll learn to optimize query performance and automate data refreshes, ensuring your analysis remains current. By the end of this guide, you'll have the confidence and expertise to effectively transform and manage data using Power Query, significantly enhancing your data analysis capabilities in Excel and Power BI.