Learn how to analyze data using Python models with the help of real-world use cases and guidance from industry experts Key Features Get to grips with data analysis by studying use cases from different fields Develop your critical thinking skills by following tried-and-true data analysis Learn how to use conclusions from data analyses to make better business decisions Book DescriptionBusinesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you'll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst. What you will learn Get to grips with the fundamental concepts and conventions of data analysis Understand how different algorithms help you to analyze the data effectively Determine the variation between groups of data using hypothesis testing Visualize your data correctly using appropriate plotting points Use correlation techniques to uncover the relationship between variables Find hidden patterns in data using advanced techniques and strategies Who this book is forThe Data Analysis Workshop is for programmers who already know how to code in Python and want to use it to perform data analysis. If you are looking to gain practical experience in data science with Python, this book is for you.

Develop blockchain application with step-by-step instructions, working example and helpful recommendations Key Features Understanding the blockchain technology from the cybersecurity perspective Developing cyber security solutions with Ethereum blockchain technology Understanding real-world deployment of blockchain based applications Book DescriptionBlockchain technology is being welcomed as one of the most revolutionary and impactful innovations of today. Blockchain technology was first identified in the world's most popular digital currency, Bitcoin, but has now changed the outlook of several organizations and empowered them to use it even for storage and transfer of value. This book will start by introducing you to the common cyberthreat landscape and common attacks such as malware, phishing, insider threats, and DDoS. The next set of chapters will help you to understand the workings of Blockchain technology, Ethereum and Hyperledger architecture and how they fit into the cybersecurity ecosystem. These chapters will also help you to write your first distributed application on Ethereum Blockchain and the Hyperledger Fabric framework. Later, you will learn about the security triad and its adaptation with Blockchain. The last set of chapters will take you through the core concepts of cybersecurity, such as DDoS protection, PKI-based identity, 2FA, and DNS security. You will learn how Blockchain plays a crucial role in transforming cybersecurity solutions. Toward the end of the book, you will also encounter some real-world deployment examples of Blockchain in security cases, and also understand the short-term challenges and future of cybersecurity with Blockchain. What you will learn Understand the cyberthreat landscape Learn about Ethereum and Hyperledger Blockchain Program Blockchain solutions Build Blockchain-based apps for 2FA, and DDoS protection Develop Blockchain-based PKI solutions and apps for storing DNS entries Challenges and the future of cybersecurity and Blockchain Who this book is forThe book is targeted towards security professionals, or any stakeholder dealing with cybersecurity who wants to understand the next-level of securing infrastructure using Blockchain. Basic understanding of Blockchain can be an added advantage.

Learn the fundamentals of clean, effective Python coding and build the practical skills to tackle your own software development or data science projects Key Features Build key Python skills with engaging development tasks and challenging activities Implement useful algorithms and write programs to solve real-world problems Apply Python in realistic data science projects and create simple machine learning models Book DescriptionHave you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python. What you will learn Write clean and well-commented code that is easy to maintain Automate essential day-to-day tasks with Python scripts Debug logical errors and handle exceptions in your programs Explore data science fundamentals and create engaging visualizations Get started with predictive machine learning Keep your development process bug-free with automated testing Who this book is forThis book is designed for anyone who is new to the Python programming language. Whether you're an aspiring software engineer or data scientist, or are just curious about learning how to code with Python, this book is for you. No prior programming experience is required.

Leverage the power of various Google Cloud AI Services by building a smart web application using MEAN Stack Key Features Start working with the Google Cloud Platform and the AI services it offers Build smart web applications by combining the power of Google Cloud AI services and the MEAN stack Build a web-based dashboard of smart applications that perform language processing, translation, and computer vision on the cloud Book DescriptionCognitive services are the new way of adding intelligence to applications and services. Now we can use Artificial Intelligence as a service that can be consumed by any application or other service, to add smartness and make the end result more practical and useful. Google Cloud AI enables you to consume Artificial Intelligence within your applications, from a REST API. Text, video and speech analysis are among the powerful machine learning features that can be used. This book is the easiest way to get started with the Google Cloud AI services suite and open up the world of smarter applications. This book will help you build a Smart Exchange, a forum application that will let you upload videos, images and perform text to speech conversions and translation services. You will use the power of Google Cloud AI Services to make our simple forum application smart by validating the images, videos, and text provided by users to Google Cloud AI Services and make sure the content which is uploaded follows the forum standards, without a human curator involvement. You will learn how to work with the Vision API, Video Intelligence API, Speech Recognition API, Cloud Language Process, and Cloud Translation API services to make your application smarter. By the end of this book, you will have a strong understanding of working with Google Cloud AI Services, and be well on the way to building smarter applications. What you will learn Understand Google Cloud Platform and its Cloud AI services Explore the Google ML Services Work with an Angular 5 MEAN stack application Integrate Vision API, Video Intelligence API for computer vision Be ready for conversational experiences with the Speech Recognition API, Cloud Language Process and Cloud Translation API services Build a smart web application that uses the power of Google Cloud AI services to make apps smarter Who this book is forThis book is ideal for data professionals and web developers who want to use the power of Google Cloud AI services in their projects, without the going through the pain of mastering machine learning for images, videos and text. Some familiarity with the Google Cloud Platform will be helpful.

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.

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.

Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet Key Features Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem Improve models using parameter tuning, feature engineering, and ensembling Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domains Book DescriptionDeep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You'll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you'll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms. What you will learn Design a feedforward neural network to see how the activation function computes an output Create an image recognition model using convolutional neural networks (CNNs) Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm Apply text cleaning techniques to remove uninformative text using NLP Build, train, and evaluate a GAN model for face generation Understand the concept and implementation of reinforcement learning in R Who this book is forThis book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.

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.

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.

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.

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.

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.

Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step approach to improving model development and performance Book DescriptionMachine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This book will help you improve your data mining techniques by using smart modeling techniques. This book will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all of these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models in order to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization. By the end of this book, you will be able to build predictive models and extract information of interest from the dataset What you will learn Hone your model-building skills and create the most accurate models Understand how predictive machine learning models work Prepare your data to acquire the best possible results Combine models in order to suit the requirements of different types of data Analyze single and multiple models and understand their combined results Derive worthwhile insights from your data using histograms and graphs Who this book is forIf you are a data scientist, data analyst, and data mining professional and are keen to achieve a 30% higher salary by adding machine learning to your skillset, then this is the ideal book for you. You will learn to apply machine learning techniques to various data mining challenges. No prior knowledge of machine learning is assumed.

Explore the entire Hyperledger blockchain family, including frameworks such as Fabric, Sawtooth, Indy, Burrow, and Iroha; and tools such as Composer, Explorer, and Caliper. Key Features Plan, design, and create a full-fledged private decentralized application using Hyperledger services Master the ins and outs of the Hyperledger network using real-world examples Packed with problem-solution-based recipes to tackle pain areas in the blockchain development cycle Book DescriptionHyperledger is an open-source project and creates private blockchain applications for a range of domains. This book will be your desk reference as you explore common and not-so-common challenges faced while building blockchain networks using Hyperledger services. We'll work through all Hyperledger platform modules to understand their services and features and build end-to-end blockchain applications using various frameworks and tools supported by Hyperledger. This book's independent, recipe-based approach (packed with real-world examples) will familiarize you with the blockchain development cycle. From modeling a business network to integrating with various tools, you will cover it all. We'll cover common and not-so-common challenges faced in the blockchain life cycle. Later, we'll delve into how we can interact with the Hyperledger Fabric blockchain, covering all the principles you need to master, such as chaincode, smart contracts, and much more. We'll also address the scalability and security issues currently faced in blockchain development. By the end of this book, you will be able to implement each recipe to plan, design, and create a full-fledged, private, decentralized application to meet organizational needs. What you will learn Create the most popular permissioned blockchain network with Fabric and Composer Build permissioned and permission-less blockchains using Sawtooth Utilize built-in Iroha asset/account management with role-based permissions Implement and run Ethereum smart contracts with Burrow Get to grips with security and scalability in Hyperledger Explore and view blockchain data using Hyperledger Explorer Produce reports containing performance indicators and benchmarks using Caliper Who this book is forThis book is for blockchain developers who want to understand how they can apply Hyperledger services in their day-to-day projects. This book uses a recipe-based approach to help you use Hyperledger to build powerful, decentralized autonomous applications. We assume the reader has a basic knowledge of the Blockchain technology and cryptography concepts

Explore powerful R packages to create predictive models using ensemble methods Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book DescriptionEnsemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques - bagging, random forest, and boosting - then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. What you will learn Carry out an essential review of re-sampling methods, bootstrap, and jackknife Explore the key ensemble methods: bagging, random forests, and boosting Use multiple algorithms to make strong predictive models Enjoy a comprehensive treatment of boosting methods Supplement methods with statistical tests, such as ROC Walk through data structures in classification, regression, survival, and time series data Use the supplied R code to implement ensemble methods Learn stacking method to combine heterogeneous machine learning models Who this book is forThis book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.

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.