A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark. Key Features Learn about the core concepts and the latest developments in Apache Spark Master writing efficient big data applications with Spark's built-in modules for SQL, Streaming, Machine Learning and Graph analysis Get introduced to a variety of optimizations based on the actual experience Book DescriptionApache Spark is a flexible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark - one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark's built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications. What you will learn Learn core concepts such as RDDs, DataFrames, transformations, and more Set up a Spark development environment Choose the right APIs for your applications Understand Spark's architecture and the execution flow of a Spark application Explore built-in modules for SQL, streaming, ML, and graph analysis Optimize your Spark job for better performance Who this book is forIf you are a big data enthusiast and love processing huge amount of data, this book is for you. If you are data engineer and looking for the best optimization techniques for your Spark applications, then you will find this book helpful. This book also helps data scientists who want to implement their machine learning algorithms in Spark. You need to have a basic understanding of any one of the programming languages such as Scala, Python or Java.

Perform advanced dashboard, visualization, and analytical techniques with Tableau Desktop, Tableau Prep, and Tableau Server Key Features Unique problem-solution approach to aid effective business decision-making Create interactive dashboards and implement powerful business intelligence solutions Includes best practices on using Tableau with modern cloud analytics services Book DescriptionTableau has been one of the most popular business intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. Tableau 2019.x Cookbook is full of useful recipes from industry experts, who will help you master Tableau skills and learn each aspect of Tableau's ecosystem. This book is enriched with features such as Tableau extracts, Tableau advanced calculations, geospatial analysis, and building dashboards. It will guide you with exciting data manipulation, storytelling, advanced filtering, expert visualization, and forecasting techniques using real-world examples. From basic functionalities of Tableau to complex deployment on Linux, you will cover it all. Moreover, you will learn advanced features of Tableau using R, Python, and various APIs. You will learn how to prepare data for analysis using the latest Tableau Prep. In the concluding chapters, you will learn how Tableau fits the modern world of analytics and works with modern data platforms such as Snowflake and Redshift. In addition, you will learn about the best practices of integrating Tableau with ETL using Matillion ETL. By the end of the book, you will be ready to tackle business intelligence challenges using Tableau's features. What you will learn Understand the basic and advanced skills of Tableau Desktop Implement best practices of visualization, dashboard, and storytelling Learn advanced analytics with the use of build in statistics Deploy the multi-node server on Linux and Windows Use Tableau with big data sources such as Hadoop, Athena, and Spectrum Cover Tableau built-in functions for forecasting using R packages Combine, shape, and clean data for analysis using Tableau Prep Extend Tableau's functionalities with REST API and R/Python Who this book is forTableau 2019.x Cookbook is for data analysts, data engineers, BI developers, and users who are looking for quick solutions to common and not-so-common problems faced while using Tableau products. Put each recipe into practice by bringing the latest offerings of Tableau 2019.x to solve real-world analytics and business intelligence challenges. Some understanding of BI concepts and Tableau is required.

Learn the core concepts of geospatial data analysis for building actionable and insightful GIS applications Key Features Create GIS solutions using the new features introduced in Python 3.7 Explore a range of GIS tools and libraries such as PostGIS, QGIS, and PROJ Learn to automate geospatial analysis workflows using Python and Jupyter Book DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data. By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel. What you will learn Automate geospatial analysis workflows using Python Code the simplest possible GIS in just 60 lines of Python Create thematic maps with Python tools such as PyShp, OGR, and the Python Imaging Library Understand the different formats that geospatial data comes in Produce elevation contours using Python tools Create flood inundation models Apply geospatial analysis to real-time data tracking and storm chasing Who this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.

Over 60 recipes to model and handle real-life biological data using modern libraries from the R ecosystem Key Features Apply modern R packages to handle biological data using real-world examples Represent biological data with advanced visualizations suitable for research and publications Handle real-world problems in bioinformatics such as next-generation sequencing, metagenomics, and automating analyses Book DescriptionHandling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you'll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you'll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data. What you will learn Employ Bioconductor to determine differential expressions in RNAseq data Run SAMtools and develop pipelines to find single nucleotide polymorphisms (SNPs) and Indels Use ggplot to create and annotate a range of visualizations Query external databases with Ensembl to find functional genomics information Execute large-scale multiple sequence alignment with DECIPHER to perform comparative genomics Use d3.js and Plotly to create dynamic and interactive web graphics Use k-nearest neighbors, support vector machines and random forests to find groups and classify data Who this book is forThis book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning through a recipe-based approach. Working knowledge of R programming language and basic knowledge of bioinformatics are prerequisites.

Learn the most powerful and primary programming language for writing smart contracts and find out how to write, deploy, and test smart contracts in Ethereum. Key Features Get you up and running with Solidity Programming language Build Ethereum Smart Contracts with Solidity as your scripting language Learn to test and deploy the smart contract to your private Blockchain Book DescriptionSolidity is a contract-oriented language whose syntax is highly influenced by JavaScript, and is designed to compile code for the Ethereum Virtual Machine. Solidity Programming Essentials will be your guide to understanding Solidity programming to build smart contracts for Ethereum and blockchain from ground-up. We begin with a brief run-through of blockchain, Ethereum, and their most important concepts or components. You will learn how to install all the necessary tools to write, test, and debug Solidity contracts on Ethereum. Then, you will explore the layout of a Solidity source file and work with the different data types. The next set of recipes will help you work with operators, control structures, and data structures while building your smart contracts. We take you through function calls, return types, function modifers, and recipes in object-oriented programming with Solidity. Learn all you can on event logging and exception handling, as well as testing and debugging smart contracts. By the end of this book, you will be able to write, deploy, and test smart contracts in Ethereum. This book will bring forth the essence of writing contracts using Solidity and also help you develop Solidity skills in no time. What you will learn Learn the basics and foundational concepts of Solidity and Ethereum Explore the Solidity language and its uniqueness in depth Create new accounts and submit transactions to blockchain Get to know the complete language in detail to write smart contracts Learn about major tools to develop and deploy smart contracts Write defensive code using exception handling and error checking Understand Truffle basics and the debugging process Who this book is forThis book is for anyone who would like to get started with Solidity Programming for developing an Ethereum smart contract. No prior knowledge of EVM is required.

Solve business challenges with Microsoft Power BI's advanced visualization and data analysis techniques Key Features Create effective storytelling reports by implementing simple-to-intermediate Power BI features Develop powerful analytical models to extract key insights for changing business needs Build, publish, and share impressive dashboards for your organization Book DescriptionTo succeed in today's transforming business world, organizations need business intelligence capabilities to make smarter decisions faster than ever before. This Power BI book is an entry-level guide that will get you up and running with data modeling, visualization, and analytical techniques from scratch. You'll find this book handy if you want to get well-versed with the extensive Power BI ecosystem. You'll start by covering the basics of business intelligence and installing Power BI. You'll then learn the wide range of Power BI features to unlock business insights. As you progress, the book will take you through how to use Power Query to ingest, cleanse, and shape your data, and use Power BI DAX to create simple to complex calculations. You'll also be able to add a variety of interactive visualizations to your reports to bring your data to life. Finally, you'll gain hands-on experience in creating visually stunning reports that speak to business decision makers, and see how you can securely share these reports and collaborate with others. By the end of this book, you'll be ready to create simple, yet effective, BI reports and dashboards using the latest features of Power BI. What you will learn Explore the different features of Power BI to create interactive dashboards Use the Query Editor to import and transform data Perform simple and complex DAX calculations to enhance analysis Discover business insights and tell a story with your data using Power BI Explore data and learn to manage datasets, dataflows, and data gateways Use workspaces to collaborate with others and publish your reports Who this book is forIf you're an IT manager, data analyst, or BI user new to using Power BI for solving business intelligence problems, this book is for you. You'll also find this book useful if you want to migrate from other BI tools to create powerful and interactive dashboards. No experience of working with Power BI is expected.

Text processing and pattern matching simplified Key Features -Master the fastest and most elegant big data munging language -Implement text processing and pattern matching using the advanced features of AWK and GAWK -Implement debugging and inter-process communication using GAWK Book DescriptionAWK is one of the most primitive and powerful utilities which exists in all Unix and Unix-like distributions. It is used as a command-line utility when performing a basic text-processing operation, and as programming language when dealing with complex text-processing and mining tasks. With this book, you will have the required expertise to practice advanced AWK programming in real-life examples. The book starts off with an introduction to AWK essentials. You will then be introduced to regular expressions, AWK variables and constants, arrays and AWK functions and more. The book then delves deeper into more complex tasks, such as printing formatted output in AWK, control flow statements, GNU's implementation of AWK covering the advanced features of GNU AWK, such as network communication, debugging, and inter-process communication in the GAWK programming language which is not easily possible with AWK. By the end of this book, the reader will have worked on the practical implementation of text processing and pattern matching using AWK to perform routine tasks. What you will learn -Create and use different expressions and control flow statements in AWK -Use Regular Expressions with AWK for effective text-processing -Use built-in and user-defined variables to write AWK programs -Use redirections in AWK programs and create structured reports -Handle non-decimal input, 2-way inter-process communication with Gawk -Create small scripts to reformat data to match patterns and process texts Who this book is forThis book is for developers or analysts who are inclined to learn how to do text processing and data extraction in a Unix-like environment. Basic understanding of Linux operating system and shell scripting will help you to get the most out of the book.

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Key Features Build your first machine learning model using scikit-learn Train supervised and unsupervised models using popular techniques such as classification, regression and clustering Understand how scikit-learn can be applied to different types of machine learning problems Book DescriptionScikit-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. What you will learn Learn how to work with all scikit-learn's machine learning algorithms Install and set up scikit-learn to build your first machine learning model Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups Perform classification and regression machine learning Use an effective pipeline to build a machine learning project from scratch Who this book is forThis book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.

A fast paced guide that will help you learn about Apache Hadoop 3 and its ecosystem Key Features Set up, configure and get started with Hadoop to get useful insights from large data sets Work with the different components of Hadoop such as MapReduce, HDFS and YARN Learn about the new features introduced in Hadoop 3 Book DescriptionApache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster. What you will learn Store and analyze data at scale using HDFS, MapReduce and YARN Install and configure Hadoop 3 in different modes Use Yarn effectively to run different applications on Hadoop based platform Understand and monitor how Hadoop cluster is managed Consume streaming data using Storm, and then analyze it using Spark Explore Apache Hadoop ecosystem components, such as Flume, Sqoop, HBase, Hive, and Kafka Who this book is forAspiring Big Data professionals who want to learn the essentials of Hadoop 3 will find this book to be useful. Existing Hadoop users who want to get up to speed with the new features introduced in Hadoop 3 will also benefit from this book. Having knowledge of Java programming will be an added advantage.

Get to grips with pandas-a versatile and high-performance Python library for data manipulation, analysis, and discovery Key Features Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains using step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool Book DescriptionData analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling in Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Who this book is forThis book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.

Solve real-world statistical problems using the most popular R packages and techniques Key Features Learn how to apply statistical methods to your everyday research with handy recipes Foster your analytical skills and interpret research across industries and business verticals Perform t-tests, chi-squared tests, and regression analysis using modern statistical techniques Book DescriptionR 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. What you will learn Become well versed with recipes that will help you interpret plots with R Formulate advanced statistical models in R to understand its concepts Perform Bayesian regression to predict models and input missing data Use time series analysis for modelling and forecasting temporal data Implement a range of regression techniques for efficient data modelling Get to grips with robust statistics and hidden Markov models Explore ANOVA (Analysis of Variance) and perform hypothesis testing Who this book is forIf you are a quantitative researcher, statistician, data analyst, or data scientist looking to tackle various challenges in statistics, this book is what you need! Proficiency in R programming and basic knowledge of linear algebra is necessary to follow along the recipes covered in this book.

Build reporting applications and dashboards using the different MicroStrategy objects Key Features Learn the fundamentals of MicroStrategy Use MicroStrategy to get actionable insights from your business data Create visualizations and build intuitive dashboards and reports Book DescriptionMicroStrategy is an enterprise business intelligence application. It turns data into reports for making and executing key organization decisions. This book shows you how to implement Business Intelligence (BI) with MicroStrategy. It takes you from setting up and configuring MicroStrategy to security and administration. The book starts by detailing the different components of the MicroStrategy platform, and the key concepts of Metadata and Project Source. You will then install and configure MicroStrategy and lay down the foundations for building MicroStrategy BI solutions. By learning about objects and different object types, you will develop a strong understanding of the MicroStrategy Schema and Public Objects. With these MicroStrategy objects, you will enhance and scale your BI and Analytics solutions. Finally, you will learn about the administration, security, and monitoring of your BI solution. What you will learn Set up the MicroStrategy Intelligence Server and client tools Create a MicroStrategy metadata repository and your first Project Explore the main MicroStrategy object types and their dependencies Create, manipulate, and share Reports Create and share Dashboards Manage Users and Groups Who this book is forThis book is for Business Intelligence professionals or data analysts who want to get started with Microstrategy. Some basic understanding of BI and data analysis will be required to get the most from this book.

Master scala's advanced techniques to solve real-world problems in data analysis and gain valuable insights from your data Key Features A beginner's guide for performing data analysis loaded with numerous rich, practical examples Access to popular Scala libraries such as Breeze, Saddle for efficient data manipulation and exploratory analysis Develop applications in Scala for real-time analysis and machine learning in Apache Spark Book DescriptionEfficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights What you will learn Techniques to determine the validity and confidence level of data Apply quartiles and n-tiles to datasets to see how data is distributed into many buckets Create data pipelines that combine multiple data lifecycle steps Use built-in features to gain a deeper understanding of the data Apply Lasso regression analysis method to your data Compare Apache Spark API with traditional Apache Spark data analysis Who this book is forIf you are a data scientist or a data analyst who wants to learn how to perform data analysis using Scala, this book is for you. All you need is knowledge of the basic fundamentals of Scala programming.

Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages Key Features Gain expertise in machine learning, deep learning and other techniques Build intelligent end-to-end projects for finance, social media, and a variety of domains Implement multi-class classification, regression, and clustering Book DescriptionR is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You'll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood. By the end of this Learning Path, you'll be equipped with the skills you need to deploy machine learning techniques in your own projects. This Learning Path includes content from the following Packt products: R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari Mastering Machine Learning with R - Third Edition by Cory Lesmeister What you will learn Develop a joke recommendation engine to recommend jokes that match users' tastes Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learning Implement NLP techniques for sentiment analysis and customer segmentation Produce simple and effective data visualizations for improved insights Use NLP to extract insights for text Implement tree-based classifiers including random forest and boosted tree Who this book is forIf you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.

Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key Features Get introduced to Python's rich suite of libraries for statistical modeling Implement regression, clustering and train neural networks from scratch Includes real-world examples on training end-to-end machine learning systems in Python Book DescriptionPython's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics. You'll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them. By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics. What you will learn Understand the importance of statistical modeling Learn about the various Python packages for statistical analysis Implement algorithms such as Naive Bayes, random forests, and more Build predictive models from scratch using Python's scikit-learn library Implement regression analysis and clustering Learn how to train a neural network in Python Who this book is forIf you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.

Teach your machine to think for itself! Key Features Delve into supervised learning and grasp how a machine learns from data Implement popular machine learning algorithms from scratch, developing a deep understanding along the way Explore some of the most popular scientific and mathematical libraries in the Python language Book DescriptionSupervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine "learns" under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you'll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learn Crack how a machine learns a concept and generalize its understanding to new data Uncover the fundamental differences between parametric and non-parametric models Implement and grok several well-known supervised learning algorithms from scratch Work with models in domains such as ecommerce and marketing Expand your expertise and use various algorithms such as regression, decision trees, and clustering Build your own models capable of making predictions Delve into the most popular approaches in deep learning such as transfer learning and neural networks Who this book is forThis book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming-and some fundamental knowledge of supervised learning-are expected.

Explore distributed ledger technology, decentralization, and smart contracts and develop real-time decentralized applications with Ethereum and Solidity Key Features Get to grips with the underlying technical principles and implementations of blockchain Build powerful applications using Ethereum to secure transactions and create smart contracts Gain advanced insights into cryptography and cryptocurrencies Book DescriptionBlockchain technology is a distributed ledger with applications in industries such as finance, government, and media. This Learning Path is your guide to building blockchain networks using Ethereum, JavaScript, and Solidity. You will get started by understanding the technical foundations of blockchain technology, including distributed systems, cryptography and how this digital ledger keeps data secure. Further into the chapters, you'll gain insights into developing applications using Ethereum and Hyperledger. As you build on your knowledge of Ether security, mining , smart contracts, and Solidity, you'll learn how to create robust and secure applications that run exactly as programmed without being affected by fraud, censorship, or third-party interference. Toward the concluding chapters, you'll explore how blockchain solutions can be implemented in applications such as IoT apps, in addition to its use in currencies. The Learning Path will also highlight how you can increase blockchain scalability and even discusses the future scope of this fascinating and powerful technology. By the end of this Learning Path, you'll be equipped with the skills you need to tackle pain points encountered in the blockchain life cycle and confidently design and deploy decentralized applications. This Learning Path includes content from the following Packt products: Mastering Blockchain - Second Edition by Imran Bashir Building Blockchain Projects by Narayan Prusty What you will learn Understand why decentralized applications are important Discover the mechanisms behind bitcoin and alternative cryptocurrencies Master how cryptography is used to secure data with the help of examples Maintain, monitor, and manage your blockchain solutions Create Ethereum wallets Explore research topics and the future scope of blockchain technology Who this book is forThis Learning Path is designed for blockchain developers who want to build decentralized applications and smart contracts from scratch using Hyperledger. Basic familiarity with any programming language will be useful to get started with this Learning Path.

Troubleshoot query performance issues, identify anti-patterns in code, and write efficient T-SQL queries Key Features Discover T-SQL functionalities and services that help you interact with relational databases Understand the roles, tasks and responsibilities of a T-SQL developer Explore solutions for carrying out database querying tasks, database administration, and troubleshooting Book DescriptionTransact-SQL (T-SQL) is Microsoft's proprietary extension to the SQL language that is used with Microsoft SQL Server and Azure SQL Database. This book will be a useful guide to learning the art of writing efficient T-SQL code in modern SQL Server versions, as well as the Azure SQL Database. The book will get you started with query processing fundamentals to help you write powerful, performant T-SQL queries. You will then focus on query execution plans and learn how to leverage them for troubleshooting. In the later chapters, you will learn how to identify various T-SQL patterns and anti-patterns. This will help you analyze execution plans to gain insights into current performance, and determine whether or not a query is scalable. You will also learn to build diagnostic queries using dynamic management views (DMVs) and dynamic management functions (DMFs) to address various challenges in T-SQL execution. Next, you will study how to leverage the built-in tools of SQL Server to shorten the time taken to address query performance and scalability issues. In the concluding chapters, the book will guide you through implementing various features, such as Extended Events, Query Store, and Query Tuning Assistant using hands-on examples. By the end of this book, you will have the skills to determine query performance bottlenecks, avoid pitfalls, and discover the anti-patterns in use. Foreword by Conor Cunningham, Partner Architect - SQL Server and Azure SQL - Microsoft What you will learn Use Query Store to understand and easily change query performance Recognize and eliminate bottlenecks that lead to slow performance Deploy quick fixes and long-term solutions to improve query performance Implement best practices to minimize performance risk using T-SQL Achieve optimal performance by ensuring careful query and index design Use the latest performance optimization features in SQL Server 2017 and SQL Server 2019 Protect query performance during upgrades to newer versions of SQL Server Who this book is forThis book is for database administrators, database developers, data analysts, data scientists, and T-SQL practitioners who want to get started with writing T-SQL code and troubleshooting query performance issues, through the help of practical examples. Previous knowledge of T-SQL querying is not required to get started on this book.

A practical guide to administer, monitor and replicate your PostgreSQL 11 database Key Features Study and apply the newly introduced features in PostgreSQL 11 Tackle any problem in PostgreSQL 11 administration and management Catch up on expert techniques for monitoring, fine-tuning, and securing your database Book DescriptionPostgreSQL is a powerful, open source database management system with an enviable reputation for high performance and stability. With many new features in its arsenal, PostgreSQL 11 allows you to scale up your PostgreSQL infrastructure. This book takes a step-by-step, recipe-based approach to effective PostgreSQL administration. The book will introduce you to new features such as logical replication, native table partitioning, additional query parallelism, and much more to help you to understand and control, crash recovery and plan backups. You will learn how to tackle a variety of problems and pain points for any database administrator such as creating tables, managing views, improving performance, and securing your database. As you make steady progress, the book will draw attention to important topics such as monitoring roles, backup, and recovery of your PostgreSQL 11 database to help you understand roles and produce a summary of log files, ensuring high availability, concurrency, and replication. By the end of this book, you will have the necessary knowledge to manage your PostgreSQL 11 database efficiently. What you will learn Troubleshoot open source PostgreSQL version 11 on various platforms Deploy best practices for planning and designing live databases Select and implement robust backup and recovery techniques in PostgreSQL 11 Use pgAdmin or OmniDB to perform database administrator (DBA) tasks Adopt efficient replication and high availability techniques in PostgreSQL Improve the performance of your PostgreSQL solution Who this book is forThis book is designed for database administrators, data architects, database developers, or anyone with an interest in planning and running live production databases using PostgreSQL 11. It is also ideal if you're looking for hands-on solutions to any problem associated with PostgreSQL 11 administration. Some experience with handling PostgreSQL databases will be beneficial

Get to grips with Kibana and its advanced functions to create interactive visualizations and dashboards Key Features Explore visualizations and perform histograms, stats, and map analytics Unleash X-Pack and Timelion, and learn alerting, monitoring, and reporting features Manage dashboards with Beats and create machine learning jobs for faster analytics Book DescriptionKibana is one of the popular tools among data enthusiasts for slicing and dicing large datasets and uncovering Business Intelligence (BI) with the help of its rich and powerful visualizations. To begin with, Mastering Kibana 6.x quickly introduces you to the features of Kibana 6.x, before teaching you how to create smart dashboards in no time. You will explore metric analytics and graph exploration, followed by understanding how to quickly customize Kibana dashboards. In addition to this, you will learn advanced analytics such as maps, hits, and list analytics. All this will help you enhance your skills in running and comparing multiple queries and filters, influencing your data visualization skills at scale. With Kibana's Timelion feature, you can analyze time series data with histograms and stats analytics. By the end of this book, you will have created a speedy machine learning job using X-Pack capabilities. What you will learn Create unique dashboards with various intuitive data visualizations Visualize Timelion expressions with added histograms and stats analytics Integrate X-Pack with your Elastic Stack in simple steps Extract data from Elasticsearch for advanced analysis and anomaly detection using dashboards Build dashboards from web applications for application logs Create monitoring and alerting dashboards using Beats Who this book is forMastering Kibana 6.x is for you if you are a big data engineer, DevOps engineer, or data scientist aspiring to go beyond data visualization at scale and gain maximum insights from their large datasets. Basic knowledge of Elasticstack will be an added advantage, although not mandatory.