R Machine Learning Essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment

Michele Usuelli

R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Dr. Sunil Kumar Chinnamgari

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.

R: Mining spatial, text, web, and social media data. Create and customize data mining algorithms

Nathan H. Danneman, Richard Heimann, Pradeepta Mishra, Bater Makhabel

Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects.After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.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:? Learning Data Mining with R by Bater Makhabel ? R Data Mining Blueprints by Pradeepta Mishra? Social Media Mining with R by Nathan Danneman and Richard Heimann

R Programming By Example. Practical, hands-on projects to help you get started with R

Omar Trejo Navarro

R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R.We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization.By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.

R Programming Fundamentals. Deal with data using various modeling techniques

Kaelen Medeiros

R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. You’ll start by understanding how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops.Once you have grasped the basics, you’ll move on to studying data visualization and graphics. You’ll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you’ll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values.By the end of this book, you’ll have completed an entire data science project of your own for your portfolio or blog.

R: Recipes for Analysis, Visualization and Machine Learning. Click here to enter text

Shanthi Viswanathan, Atmajitsinh Gohil, Viswa Viswanathan, Yu-Wei, ...

The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We’ll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We’ll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we’ll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.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:• R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan• R Data Visualization Cookbook by Atmajitsinh Gohil• Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)

R Statistics Cookbook. Over 100 recipes for performing complex statistical operations with R 3.5

Francisco Juretig

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools.You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making.By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.

R: Unleash Machine Learning Techniques. Smarter data analytics

Brett Lantz, Cory Lesmeister, Dipanjan Sarkar, Raghav Bali

R is the established language of data analysts and statisticians around the world. And you shouldn’t be afraid to use it…This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you’ll get to grips with the fundamentals of R. This means you’ll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.For the following two modules we’ll begin to investigate machine learning algorithms in more detail. To build upon the basics, you’ll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they’re all focused on solving real problems in different areas, ranging from finance to social media.This Learning Path has been curated from three Packt products:• R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar• Machine Learning with R - Second Edition By Brett Lantz• Mastering Machine Learning with R By Cory Lesmeister

R Web Scraping Quick Start Guide. Techniques and tools to crawl and scrape data from websites

Olgun Aydin

Web scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming.You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules. We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R.

Raportowanie w System Center Configuration Manager Bez tajemnic

Garth Jones, Dan Toll, Kerrie Meyler

Baza danych SQL Server programu Microsoft System Center Configuration Manager (ConfigMgr) zawiera wiele cennych informacji na temat Twoich użytkowników, komputerów, sprzętu, systemów operacyjnych, aplikacji czy stanu zgodności. Aby umożliwić Ci efektywne wyodrębnianie tych danych, Microsoft dostarczył kilku doskonałych narzędzi, wliczając w to usługi raportowania SQL Server Reporting Services (SSRS) i dodatek SQL Server Data Tools Business Intelligence (SSDT-BI). Podręcznik Raportowanie w System Center Configuration Manager bez tajemnic pokaże Ci, w jaki sposób możesz wykorzystać maksymalny potencjał tych narzędzi. Światowej sławy guru raportowania, Garth Jones, wraz z będącymi ekspertami współautorami tego przewodnika poprowadzi Cię przez wszystkie aspekty niestandardowego raportowania w System Center. Poczynając od instalacji i konfiguracji usług SSRS, krok po kroku nauczysz się wykorzystywać widoki języka SQL do wyszukiwania potrzebnych Ci danych, budować zapytania SQL, tworzyć proste i zaawansowane raporty, a także wykorzystywać administrację opartą na rolach do bezpiecznego dostarczania tych raportów właściwym osobom. W książce tej Jones zebrał aktualne, niezawodne i wszechstronne techniki raportowania w System Center, których na próżno szukać w innych podręcznikach i witrynach internetowych. Korzystając z tego przewodnika będziesz w stanie konsekwentnie pozyskiwać właściwe informacje, które pozwolą Ci rozwiązywać palące problemy i szybko reagować na ewentualne obawy zarządu. Garth Jones, główny architekt w Enhansoft i Microsoft MVP, specjalizuje się w poszerzaniu wartości i znaczenia programu System Center Configuration Manager. Z rodziną produktów System Center pracuje od roku 1996, kiedy to występowała jeszcze pod nazwą SMS. Dan Toll jest administratorem programu Configuration Manager, z którym pracuje od wersji SMS 2003. Specjalizuje się we wdrożeniach systemów operacyjnych dla stacji roboczych i serwerów przy użyciu narzędzi Microsoft Deployment Toolkit (MDT) oraz w raportowaniu w programie ConfigMgr. Kerrie Meyler, Microsoft MVP, jest wiodącą autorką wielu książek z serii System Center Unleashed. Obecnie pracuje jako niezależny konsultant. W czasie trwającej ponad 17 lat kariery zawodowej ewangelizowała produkt SMS na stanowisku starszego specjalisty technologii w Microsoft i prezentowała technologie System Center na konferencjach TechEd i MMS. Szczegółowe informacje na temat Instalowania i konfigurowania usług SSRS pod kątem optymalnego raportowania w System Center i łatwiej-szego rozwiązywania problemów Danych przechowywanych w bazie lokacji programu ConfigMgr Wydajnego pozyskiwania danych programu ConfigMgr poprzez tworzenie zapytań SQL z poziomu SQL Server Management Studio Najlepszych praktyk w zakresie tworzenia i projektowania raportów w System Center Tworzenia szablonów raportów, dostosowywania treści z użyciem parametrów raportów oraz zagnieżdżania wykresów Dostosowywania logo, palet kolorów i pozostałych elementów raportów na potrzeby konkretnej organizacji Konstruowania zaawansowanych metod przeglądania szczegółowego w celu dostarczenia dodatkowych informacji Wzmacniania zabezpieczeń raportów poprzez integrowanie administracji programu ConfigMgr opartej na rolach w zapytaniach SQL Wykorzystywania raportowania do pomiaru kluczowych wskaźników wydajności i pogłębiania wiedzy na temat własnego środowiska Dostosowywania raportów do potrzeb użytkowników końcowych lub zarządu W SIECI: Wszystkie zaprezentowane w tej książce przykłady i skrypty dostępne są do pobrania na stronie informit.com/title/9780672337789

Reactive Programming in Kotlin. Design and build non-blocking, asynchronous Kotlin applications with RXKotlin, Reactor-Kotlin, Android, and Spring

Rivu Chakraborty

In today's app-driven era, when programs are asynchronous, and responsiveness is so vital, reactive programming can help you write code that's more reliable, easier to scale, and better-performing. Reactive programming is revolutionary.With this practical book, Kotlin developers will first learn how to view problems in the reactive way, and then build programs that leverage the best features of this exciting new programming paradigm. You will begin with the general concepts of Reactive programming and then gradually move on to working with asynchronous data streams. You will dive into advanced techniques such as manipulating time in data-flow, customizing operators and provider and how to use the concurrency model to control asynchronicity of code and process event handlers effectively.You will then be introduced to functional reactive programming and will learn to apply FRP in practical use cases in Kotlin. This book will also take you one step forward by introducing you to Spring 5 and Spring Boot 2 using Kotlin. By the end of the book, you will be able to build real-world applications with reactive user interfaces as well as you'll learn to implement reactive programming paradigms in Android.

Real-time Analytics with Storm and Cassandra. Solve real-time analytics problems effectively using Storm and Cassandra

Shilpi Saxena

Real-Time Big Data Analytics. Design, process, and analyze large sets of complex data in real time

Sumit Gupta, Shilpi Saxena

Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time.Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases.From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm.Moving on, we’ll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program. You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark.At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data.

Recurrent Neural Networks with Python Quick Start Guide. Sequential learning and language modeling with TensorFlow

Simeon Kostadinov

Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling.Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood.After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field.

Red Hat Certified Specialist in Services Management and Automation EX358 Exam Guide. Get your certification and prepare for real-world challenges as a Red Hat Certified Specialist

Eric McLeroy, Peter Sprygada

If you’re ready to take the next step in your system engineering career with the EX358, then this book is for you. Packed with all the knowledge and skills that you need to configure and maintain services and applications on the Red Hat Linux 8 (RHEL OS 8) platform, this book will help you ace the exam and thrive at work.Red Hat Certified Specialist in Service Management and Automation will help you build a solid foundation of the most recent and up-to-date exam requirements and practice questions.Throughout the course of the book, you’ll get hands-on experience with different technical processes needed to fully administer a Red Hat Enterprise Linux 8 system. This will include file storage, database management, direct configuration of applications, such as SMB shares, networking. You’ll be well equipped with the configuration of essential components like firewall, SELinux, and iSCSI while learning how to automate these tasks using Ansible Automation 2.9 in order to alleviate the burden of completing them by hand.By the end of this book, you'll have covered all essential topics to ace the Red Hat EX358 certification exam and add another feather to your career as a Red Hat Certified Specialist.

Redash v5 Quick Start Guide. Create and share interactive dashboards using Redash

Alexander Leibzon, Yael Leibzon

Data exploration and visualization is vital to Business Intelligence, the backbone of almost every enterprise or organization. Redash is a querying and visualization tool developed to simplify how marketing and business development departments are exposed to data. If you want to learn to create interactive dashboards with Redash, explore different visualizations, and share the insights with your peers, then this is the ideal book for you.The book starts with essential Business Intelligence concepts that are at the heart of data visualizations. You will learn how to find your way round Redash and its rich array of data visualization options for building interactive dashboards. You will learn how to create data storytelling and share these with peers. You will see how to connect to different data sources to process complex data, and then visualize this data to reveal valuable insights. By the end of this book, you will be confident with the Redash dashboarding tool to provide insight and communicate data storytelling.

Redis Essentials. Harness the power of Redis to integrate and manage your projects efficiently

Fredrik Sandell, Alexander Myltsev, Javier de la Rosa, Rodrigo Martin, ...

Redis is the most popular in-memory key-value data store. It's very lightweight and its data types give it an edge over the other competitors. If you need an in-memory database or a high-performance cache system that is simple to use and highly scalable, Redis is what you need.Redis Essentials is a fast-paced guide that teaches the fundamentals on data types, explains how to manage data through commands, and shares experiences from big players in the industry.We start off by explaining the basics of Redis followed by the various data types such as Strings, hashes, lists, and more. Next, Common pitfalls for various scenarios are described, followed by solutions to ensure you do not fall into common traps.After this, major differences between client implementations in PHP, Python, and Ruby are presented. Next, you will learn how to extend Redis with Lua, get to know security techniques such as basic authorization, firewall rules, and SSL encryption, and discover how to use Twemproxy, Redis Sentinel, and Redis Cluster to scale infrastructures horizontally. At the end of this book, you will be able to utilize all the essential features of Redis to optimize your project's performance.

Redmine Plugin Extension and Development. If you’d like to customize Redmine to meet your own precise project management needs, this is the ideal guide to understanding and realizing the full potential of plugins. Full of real-world examples and clear instructions

Alex Bevilacqua

Regression Analysis with Python. Discover everything you need to know about the art of regression analysis with Python, and change how you view data

Luca Massaron, Alberto Boschetti

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

Regression Analysis with R. Design and develop statistical nodes to identify unique relationships within data at scale

Giuseppe Ciaburro

Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables.This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples.By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.

Reinforcement Learning with TensorFlow. A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

Sayon Dutta

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions.The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.

Reklamuj się z Google. Skuteczne rozwiązania i przykłady

Kristina Cutura

Skuteczne kampanie na platformie Google Adwords! Google Adwords to skuteczne i niezwykle elastyczne narzędzie, dzięki któremu każdy może zareklamować się w sieci. Pozwala na dotarcie do pożądanej grupy docelowej, odgórne ustalenie ponoszonych kosztów oraz czasowe zaplanowanie emisji reklam. Ale to nie wszystko! Google Adwords to dziesiątki opcji, które mają znaczący wpływ na skuteczność Twojej reklamy. Dowiedz się, jak optymalnie wykorzystać Twoje środki na reklamę. W tej książce znajdziesz ponad sto najlepszych porad, które pozwolą Ci zoptymalizować Twoje działania reklamowe. W trakcie lektury nauczysz się badać rynek i konkurencję oraz ustalać cele. Dowiesz się, jak założyć konto w Google Adwords oraz przygotować pierwszą kampanię reklamową. Istotne jest, żebyś wiedział, jak wielu użytkowników klika Twoje reklamy oraz jak duży zysk to przynosi. Analizy współczynników konwersji pomogą Ci odpowiedzieć na to i wiele innych pytań. W kolejnych rozdziałach zobaczysz, w jaki sposób wybierać słowa kluczowe, określać budżety i stawki oraz korzystać z remarketingu. Książka ta jest bogatym źródłem informacji dla wszystkich Czytelników mających ambicję poprowadzić własną, skuteczną kampanię reklamową w Google Adwords. Dzięki tej książce: założysz konto w Google Adwords przygotujesz pierwszą kampanię reklamową zmierzysz skuteczność reklamy wykorzystasz możliwości remarketingu poznasz tajniki Google Adwords Wykorzystaj potencjał reklam Google Adwords!

Rekrutacja w IT

Michał Kosiorek

Najlepsza praca świata? Oczywiście rekruter w IT! Branża IT jest dość nietypowa. Poza nielicznymi wyjątkami ― jak specjaliści od sprzętu ― większość osób realizujących projekty informatyczne, tworzących oprogramowania i podtrzymujących działanie systemów może pracować zdalnie. Co więcej, zdalnie oznacza czasem operowanie z drugiego końca świata, w ramach międzynarodowych zespołów, których członkowie spotykają się via komunikatory i na zebraniach online. I wszystko to potrafi zadziwiająco sprawnie funkcjonować! Skoro firmy z branży informatycznej są tak bardzo "zdalne", może także pracownicy HR, może również rekruterzy byliby w stanie funkcjonować bez codziennego pojawiania się w biurze? Czytać maile na plaży na Bali, Zanzibarze czy choćby we Włoszech . Realizować powierzone sobie zadania w sposób uporządkowany i systematyczny o godzinie, która najbardziej im odpowiada. I zarabiać przy tym kwoty rzędu kilkudziesięciu tysięcy na miesiąc. Jeśli taka wizja pracy w roli rekrutera w IT do Ciebie przemawia i zastanawiasz się, jak zmienić tę wizję w rzeczywistość, to przestań marzyć, a zacznij czytać tę książkę. Jej autor konkretnie, krok po kroku pokaże Ci, co musisz zrobić, by dołączyć do światowej armii digital recruiter nomads i pracować, skąd chcesz, jak chcesz i w godzinach, które Ci odpowiadają. Zobacz zapis z webinaru prowadzonego przez Michała Kosiorka oraz Krzysztofa Kempińskiego na temat: „Marka osobista na rynku pracy w IT” ⤵️  

Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0

Svetlana Karslioglu

Pachyderm is an open source project that enables data scientists to run reproducible data pipelines and scale them to an enterprise level. This book will teach you how to implement Pachyderm to create collaborative data science workflows and reproduce your ML experiments at scale.You’ll begin your journey by exploring the importance of data reproducibility and comparing different data science platforms. Next, you’ll explore how Pachyderm fits into the picture and its significance, followed by learning how to install Pachyderm locally on your computer or a cloud platform of your choice. You’ll then discover the architectural components and Pachyderm's main pipeline principles and concepts. The book demonstrates how to use Pachyderm components to create your first data pipeline and advances to cover common operations involving data, such as uploading data to and from Pachyderm to create more complex pipelines. Based on what you've learned, you'll develop an end-to-end ML workflow, before trying out the hyperparameter tuning technique and the different supported Pachyderm language clients. Finally, you’ll learn how to use a SaaS version of Pachyderm with Pachyderm Notebooks.By the end of this book, you will learn all aspects of running your data pipelines in Pachyderm and manage them on a day-to-day basis.