Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book DescriptionData science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is forThis book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading strategies Key Features Understand the power of algorithmic trading in financial markets with real-world examples Get up and running with the algorithms used to carry out algorithmic trading Learn to build your own algorithmic trading robots which require no human intervention Book DescriptionIt's now harder than ever to get a significant edge over competitors in terms of speed and efficiency when it comes to algorithmic trading. Relying on sophisticated trading signals, predictive models and strategies can make all the difference. This book will guide you through these aspects, giving you insights into how modern electronic trading markets and participants operate. You'll start with an introduction to algorithmic trading, along with setting up the environment required to perform the tasks in the book. You'll explore the key components of an algorithmic trading business and aspects you'll need to take into account before starting an automated trading project. Next, you'll focus on designing, building and operating the components required for developing a practical and profitable algorithmic trading business. Later, you'll learn how quantitative trading signals and strategies are developed, and also implement and analyze sophisticated trading strategies such as volatility strategies, economic release strategies, and statistical arbitrage. Finally, you'll create a trading bot from scratch using the algorithms built in the previous sections. By the end of this book, you'll be well-versed with electronic trading markets and have learned to implement, evaluate and safely operate algorithmic trading strategies in live markets. What you will learn Understand the components of modern algorithmic trading systems and strategies Apply machine learning in algorithmic trading signals and strategies using Python Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more Quantify and build a risk management system for Python trading strategies Build a backtester to run simulated trading strategies for improving the performance of your trading bot Deploy and incorporate trading strategies in the live market to maintain and improve profitability Who this book is forThis book is for software engineers, financial traders, data analysts, and entrepreneurs. Anyone who wants to get started with algorithmic trading and understand how it works; and learn the components of a trading system, protocols and algorithms required for black box and gray box trading, and techniques for building a completely automated and profitable trading business will also find this book useful.

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.

Document the architecture of your software easily with this highly practical, open-source template. Key Features Get to grips with leveraging the features of arc42 to create insightful documents Learn the concepts of software architecture documentation through real-world examples Discover techniques to create compact, helpful, and easy-to-read documentation Book DescriptionWhen developers document the architecture of their systems, they often invent their own specific ways of articulating structures, designs, concepts, and decisions. What they need is a template that enables simple and efficient software architecture documentation. arc42 by Example shows how it's done through several real-world examples. Each example in the book, whether it is a chess engine, a huge CRM system, or a cool web system, starts with a brief description of the problem domain and the quality requirements. Then, you'll discover the system context with all the external interfaces. You'll dive into an overview of the solution strategy to implement the building blocks and runtime scenarios. The later chapters also explain various cross-cutting concerns and how they affect other aspects of a program. What you will learn Utilize arc42 to document a system's physical infrastructure Learn how to identify a system's scope and boundaries Break a system down into building blocks and illustrate the relationships between them Discover how to describe the runtime behavior of a system Know how to document design decisions and their reasons Explore the risks and technical debt of your system Who this book is forThis book is for software developers and solutions architects who are looking for an easy, open-source tool to document their systems. It is a useful reference for those who are already using arc42. If you are new to arc42, this book is a great learning resource. For those of you who want to write better technical documentation will benefit from the general concepts covered in this book.

Leverage the full potential of SAS to get unique, actionable insights from your data Key Features Build enterprise-class data solutions using SAS and become well-versed in SAS programming Work with different data structures, and run SQL queries to manipulate your data Explore essential concepts and techniques with practical examples to confidently pass the SAS certification exam Book DescriptionSAS is one of the leading enterprise tools in the world today when it comes to data management and analysis. It enables the fast and easy processing of data and helps you gain valuable business insights for effective decision-making. This book will serve as a comprehensive guide that will prepare you for the SAS certification exam. After a quick overview of the SAS architecture and components, the book will take you through the different approaches to importing and reading data from different sources using SAS. You will then cover SAS Base and 4GL, understanding data management and analysis, along with exploring SAS functions for data manipulation and transformation. Next, you'll discover SQL procedures and get up to speed on creating and validating queries. In the concluding chapters, you'll learn all about data visualization, right from creating bar charts and sample geographic maps through to assigning patterns and formats. In addition to this, the book will focus on macro programming and its advanced aspects. By the end of this book, you will be well versed in SAS programming and have the skills you need to easily handle and manage your data-related problems in SAS. What you will learn Explore a variety of SAS modules and packages for efficient data analysis Use SAS 4GL functions to manipulate, merge, sort, and transform data Gain useful insights into advanced PROC SQL options in SAS to interact with data Get to grips with SAS Macro and define your own macros to share data Discover the different graphical libraries to shape and visualize data with Apply the SAS Output Delivery System to prepare detailed reports Who this book is forBudding or experienced data professionals who want to get started with SAS will benefit from this book. Those looking to prepare for the SAS certification exam will also find this book to be a useful resource. Some understanding of basic data management concepts will help you get the most out of this book.

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.

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.

A practical, skill-based introduction to data analysis and literacy We are swimming in a world of data, and this handy guide will keep you afloat while you learn to make sense of it all. In Data Literacy: A User's Guide, David Herzog, a journalist with a decade of experience using data analysis to transform information into captivating storytelling, introduces students and professionals to the fundamentals of data literacy, a key skill in today's world. Assuming the reader has no advanced knowledge of data analysis or statistics, this book shows how to create insight from publicly-available data through exercises using simple Excel functions. Extensively illustrated, step-by-step instructions within a concise, yet comprehensive, reference will help readers identify, obtain, evaluate, clean, analyze and visualize data. A concluding chapter introduces more sophisticated data analysis methods and tools including database managers such as Microsoft Access and MySQL and standalone statistical programs such as SPSS, SAS and R.

Discover the story of your data using the essential elements of associations and correlations Key Features Get a comprehensive introduction to associations and correlations Explore multivariate analysis, understand its limitations, and discover the assumptions on which it's based Gain insights into the various ways of preparing your data for analysis and visualization Book DescriptionAssociations and correlations are ways of describing how a pair of variables change together as a result of their connection. By knowing the various available techniques, you can easily and accurately discover and visualize the relationships in your data. This book begins by showing you how to classify your data into the four distinct types that you are likely to have in your dataset. Then, with easy-to-understand examples, you'll learn when to use the various univariate and multivariate statistical tests. You'll also discover what to do when your univariate and multivariate results do not match. As the book progresses, it describes why univariate and multivariate techniques should be used as a tag team, and also introduces you to the techniques of visualizing the story of your data. By the end of the book, you'll know exactly how to select the most appropriate univariate and multivariate tests, and be able to use a single strategic framework to discover the true story of your data. What you will learn Identify a dataset that's fit for analysis using its basic features Understand the importance of associations and correlations Use multivariate and univariate statistical tests to confirm relationships Classify data as qualitative or quantitative and then into the four subtypes Build a visual representation of all the relationships in the dataset Automate associations and correlations with CorrelViz Who this book is forThis is a book for beginners - if you're a novice data analyst or data scientist, then this is a great place to start. Experienced data analysts might also find value in this title, as it will recap the basics and strengthen your understanding of key concepts. This book focuses on introducing the essential elements of association and correlation analysis.

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.

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.

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key Features Speed up your data analysis projects using powerful R packages and techniques Create multiple hands-on data analysis projects using real-world data Discover and practice graphical exploratory analysis techniques across domains Book DescriptionHands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process-data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learn Learn powerful R techniques to speed up your data analysis projects Import, clean, and explore data using powerful R packages Practice graphical exploratory analysis techniques Create informative data analysis reports using ggplot2 Identify and clean missing and erroneous data Explore data analysis techniques to analyze multi-factor datasets Who this book is forHands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key Features Manipulate location-based data and create intelligent geospatial data models Build effective location recommendation systems used by popular companies such as Uber A hands-on guide to help you consume spatial data and parallelize GIS operations effectively Book DescriptionData scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learn Learn how companies now use location data Set up your Python environment and install Python geospatial packages Visualize spatial data as graphs Extract geometry from spatial data Perform spatial regression from scratch Build web applications which dynamically references geospatial data Who this book is forData Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.

A beginner's guide to storing, managing, and analyzing data with the updated features of Elastic 7.0 Key Features Gain access to new features and updates introduced in Elastic Stack 7.0 Grasp the fundamentals of Elastic Stack including Elasticsearch, Logstash, and Kibana Explore useful tips for using Elastic Cloud and deploying Elastic Stack in production environments Book DescriptionThe Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and help you use it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools, and exploring their basic configurations. You'll then get up to speed with using Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You'll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you'll be well versed with the fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems. What you will learn Install and configure an Elasticsearch architecture Solve the full-text search problem with Elasticsearch Discover powerful analytics capabilities through aggregations using Elasticsearch Build a data pipeline to transfer data from a variety of sources into Elasticsearch for analysis Create interactive dashboards for effective storytelling with your data using Kibana Learn how to secure, monitor and use Elastic Stack's alerting and reporting capabilities Take applications to an on-premise or cloud-based production environment with Elastic Stack Who this book is forThis book is for entry-level data professionals, software engineers, e-commerce developers, and full-stack developers who want to learn about Elastic Stack and how the real-time processing and search engine works for business analytics and enterprise search applications. Previous experience with Elastic Stack is not required, however knowledge of data warehousing and database concepts will be helpful.

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.

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.

This book documents recent attempts to conduct systematic, prodigious and multidisciplinary research in learning analytics and present their findings and identify areas for further research and development. The book also unveils the distinguished and exemplary works by educators and researchers in the field highlighting the current trends, privacy and ethical issues, creative and unique approaches, innovative methods, frameworks, and theoretical and practical aspects of learning analytics.

Understand, design, and create cognitive applications using Watson's suite of APIs. Key Features Develop your skills and work with IBM Watson APIs to build efficient and powerful cognitive apps Learn how to build smart apps to carry out different sets of activities using real-world use cases Get well versed with the best practices of IBM Watson and implement them in your daily work Book DescriptionCognitive computing is rapidly infusing every aspect of our lives riding on three important fields: data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system grows. This book introduces readers to a whole new paradigm of computing - a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI through the set of APIs provided by IBM Watson. This book will help you build your own applications to understand, plan, and solve problems, and analyze them as per your needs. You will learn about various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems, using different IBM Watson APIs. From this, the reader will learn what ML is, and what goes on in the background to make computers "do their magic," as well as where these concepts have been applied. Having achieved this, the readers will then be able to embark on their journey of learning, researching, and applying the concept in their respective fields. What you will learn Get well versed with the APIs provided by IBM Watson on IBM Cloud Learn ML, AI, cognitive computing, and neural network principles Implement smart applications in fields such as healthcare, entertainment, security, and more Understand unstructured content using cognitive metadata with the help of Natural Language Understanding Use Watson's APIs to create real-life applications to realize their capabilities Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more Who this book is forThis book is for beginners and novices; having some knowledge about artificial intelligence and deep learning is an advantage, but not a prerequisite to benefit from this book. We explain the concept of deep learning and artificial intelligence through the set of tools IBM Watson provides.