455 Developer courses in Sheffield delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This is an introductory-level XML training course, designed for those needing an introduction to concepts and technologies associated with XML and its related recommendations. Previous experience or knowledge of HTML is helpful but not essential. Overview This course is an intensive, hands-on introduction to XML, XPath, and XSLT. The course is a balanced mixture of theory and practical labs designed to take students from the basic fundamentals of XML through to the related advanced technologies. The students walk through the different standards in a structured manner to enable them to master the concepts and ideas, which are reinforced in the lab exercises. The course starts with the fundamentals of XML, including coverage of DTDs and XML Schema. It then moves on to the XPath and XSLT standards, and how to use them to transform XML documents into other documents such as HTML documents or other XML documents. This course provides indoctrination in the practical use of W3C standards (including XSL and XML Schema) and of implementing tools and technologies. This course is programming language independent, making it useful for Java, .NET, C++, and any other programming orientation. Graduates will hit the ground running, applying XML to projects at both an architectural as well as a line by line coding level. We can easily adapt this course to industry and client specific needs.In addition to valuable knowledge and working examples, students receive a copy of the 'Xtensil' product. This unique software was developed to assist in implementing, testing, and fielding XML applications. Xtensil is used as both a teaching aid and a straightforward, basic, fully functional XML toolkit that students can use on Windows and Linux platforms. Working in a hands-on learning environment student will learn to: Write well-formed XML documents Model business requirements using XML Handle XML reserved characters Validate an XML document with a DTD and with a Schema Centralize data and markup definition with entities Create DTDs and Schemas using XML tools Generate XML documents from databases Write XSL templates to transform XML documents into HTML Integrate XML, XSL and the DOM to implement a complete solution The Extensible Markup Language (XML) is a standard that is enabling a revolution in web applications and business to business interactions. XML is the basis for Wireless Markup Language (WML), Voice Markup Language (VoiceML), Simple Object Access Protocol (SOAP), Web Services, and numerous industry initiatives such as ACORD (insurance), PXML (proposal/RFP) and OTA (travel). Introduction to XML is a three-day, hands-on course geared for software developers who need to understand what XML is and how to use in with today's systems and architectures. This course covers the topics from tags to architectures. The course is a balanced mixture of theory and practical labs designed to take students from a quick review of the basic fundamentals of XML through to the related advanced technologies. The students walk through the different standards in a structured manner to enable them to master the concepts and ideas, which are reinforced in the lab exercises. The course starts with a quick review of the fundamentals of XML before covering XML Schema in detail. It then moves on to the XPath and XSLT covering advanced topics in both. Finally, XML and Web Services security mechanisms and issues are addressed. XML Content Introduction to XML XML Mechanics XML Structure Namespaces Structure Using Schemas XML Formatting CSS and Rendering XML XSL Transformations XSLT and XPath XPath 2.0 and XSLT 2.0 Overview XSL FO (Formatting Objects) Applying XML XML Interoperability XML Performance Improvements Web Services Overview XML Applications Additional course details: Nexus Humans Introduction to XML (TT4300) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Introduction to XML (TT4300) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for The introductory-level course is geared for software developers, project managers, and IT professionals seeking to enhance their understanding and practical skills in version control and collaboration using GitLab. It's also well-suited for those transitioning from another version control system to GitLab, or those responsible for software development lifecycle within their organization. Whether you are an individual looking to boost your proficiency or a team leader aiming to drive productivity and collaboration, this course will provide the necessary expertise to make the most of GitLab's capabilities. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Gain a firm understanding of the fundamentals of Git and GitLab, setting a solid foundation for advanced concepts. Learn to effectively manage and track changes in your code, ensuring a clean and reliable codebase. Discover ways to streamline your daily tasks with aliases, stashing, and other GitLab workflow optimization techniques. Develop skills in creating, merging, and synchronizing branches, enabling seamless collaboration and version control. Equip yourself with the knowledge to use Git as a powerful debugging tool, saving time and effort when troubleshooting issues. Understand the basics of continuous integration and continuous deployment (CI/CD) in GitLab, helping you automate the software delivery process. Immerse yourself in the dynamic world of GitLab, a leading web-based platform for version control and collaboration, through our intensive two-day course, GitLab Quick Start. Version control systems, such as GitLab, are the backbone of modern software development, enabling teams to work cohesively and maintain a structured workflow. By mastering GitLab, you can improve efficiency, encourage collaboration, and ensure accuracy and reliability within your projects, adding significant value to your organization. Throughout the course you?ll explore various aspects of GitLab, starting from the fundamental principles of source code management to advanced concepts like rebasing and continuous integration/design. Key topics covered include Git and GitLab basics, reviewing and editing commit history, mastering GitFlow and GitLab Flow, branching and merging strategies, and understanding remote repositories. You'll also learn how to utilize Git as a debugging tool and explore the power of GitLab's built-in CI/CD capabilities. The core value of this course lies in its practical application. You'll learn how to effectively manage changes in code with GitLab, allowing you to maintain audit trails, create reproducible software, and seamlessly move from another version control system. Then you?ll learn how to enhance your workflow efficiency using aliases for common commands, saving changes for later use, and ignoring build artifacts. You?ll also explore GitLab's CI/CD, which will enable you to automate your software delivery process. These hands-on labs will walk you through creating, merging, and synchronizing remote branches, configuring Git, troubleshooting using Git as a debugging tool, and setting up GitLab Runner for CI/CD. Each lab is designed to simulate real-world projects, offering you a first-hand experience in managing and contributing to a version control system like GitLab. Introduction to Source Code Management The Core Principles of Change Management The Power to Undo Changes Audit Trails and Investigations Reproducible Software Changing code-hosting platform Moving from another version control system Git and GitLab Introduction and Basics Introduction to Git GitFlow GitLab Flow Trees and Commits Configuring Git Adding, Renaming, and Removing Files Reviewing and Editing the Commit History Reviewing the Commit History Revision Shortcuts Fixing Mistakes Improving Your Daily Workflow Simplifying Common Commands with Aliases Ignoring Build Artifacts Saving Changes for Later Use (Stashing) Branching Branching Basics Listing Differences Between Branches Visualizing Branches Deleting Branches Tagging Merging Merging Basics Merge Conflicts Merging Remote Branches Remote Repositories Remote Repositories Synchronizing Objects with Remotes Tracking Branches Centralizing and Controlling Access Introduction to GitLab Git Repositories on GitLab Daily Workflow Reviewing Branching and Merging Branch Review Merging Basics Rebasing Rebasing Basics Rebasing with Local Branches Rebasing with Remote Branches Interactive Rebasing Squashing Commits Getting Out of Trouble Git as a Debugging Tool Using the Blame Command to See File History Performing a Binary Search Continuous Integration / Continuous Design (CI/CD) How to install GitLab Runner Adding to our example project Breaking down .gitlab-ci.yml Adding .gitlab-ci.yml to our example project Deconstructing an advanced .gitlab-ci.yml file GitLab CI/CD web UI Optional: Resetting Trees Introduction to Resetting Resetting Branch Pointers Resetting Branches and the Index Resetting the Working Directory Making Good Use of the Reset Command Optional More on Improving Your Daily Workflow Interactively Staging Changes Optional: Including External Repositories Submodules Subtrees Choosing Between Submodules and Subtrees Workflow Management Branch Management

Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following job roles: [Cloud] information security analysts, architects, and engineers Information security/cybersecurity specialists Cloud infrastructure architects Additionally, the course is intended for Google and partner field personnel who work with customers in those job roles. The course should also be useful to developers of cloud applications Overview This course teaches participants the following skills: Understanding the Google approach to security Managing administrative identities using Cloud Identity. Implementing least privilege administrative access using Google Cloud Resource Manager, Cloud IAM. Implementing IP traffic controls using VPC firewalls and Cloud Armor Implementing Identity Aware Proxy Analyzing changes to the configuration or metadata of resources with GCP audit logs Scanning for and redact sensitive data with the Data Loss Prevention API Scanning a GCP deployment with Forseti Remediating important types of vulnerabilities, especially in public access to data and VMs This course gives participants broad study of security controls and techniques on Google Cloud Platform. Through lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution. Participants also learn mitigation techniques for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. Foundations of GCP Security Google Cloud's approach to security The shared security responsibility model Threats mitigated by Google and by GCP Access Transparency Cloud Identity Cloud Identity Syncing with Microsoft Active Directory Choosing between Google authentication and SAML-based SSO GCP best practices Identity and Access Management GCP Resource Manager: projects, folders, and organizations GCP IAM roles, including custom roles GCP IAM policies, including organization policies GCP IAM best practices Configuring Google Virtual Private Cloud for Isolation and Security Configuring VPC firewalls (both ingress and egress rules) Load balancing and SSL policies Private Google API access SSL proxy use Best practices for structuring VPC networks Best security practices for VPNs Security considerations for interconnect and peering options Available security products from partners Monitoring, Logging, Auditing, and Scanning Stackdriver monitoring and logging VPC flow logs Cloud audit logging Deploying and Using Forseti Securing Compute Engine: techniques and best practices Compute Engine service accounts, default and customer-defined IAM roles for VMs API scopes for VMs Managing SSH keys for Linux VMs Managing RDP logins for Windows VMs Organization policy controls: trusted images, public IP address, disabling serial port Encrypting VM images with customer-managed encryption keys and with customer-supplied encryption keys Finding and remediating public access to VMs VM best practices Encrypting VM disks with customer-supplied encryption keys Securing cloud data: techniques and best practices Cloud Storage and IAM permissions Cloud Storage and ACLs Auditing cloud data, including finding and remediating publicly accessible data Signed Cloud Storage URLs Signed policy documents Encrypting Cloud Storage objects with customer-managed encryption keys and with customer-supplied encryption keys Best practices, including deleting archived versions of objects after key rotation BigQuery authorized views BigQuery IAM roles Best practices, including preferring IAM permissions over ACLs Protecting against Distributed Denial of Service Attacks: techniques and best practices How DDoS attacks work Mitigations: GCLB, Cloud CDN, autoscaling, VPC ingress and egress firewalls, Cloud Armor Types of complementary partner products Application Security: techniques and best practices Types of application security vulnerabilities DoS protections in App Engine and Cloud Functions Cloud Security Scanner Threat: Identity and Oauth phishing Identity Aware Proxy Content-related vulnerabilities: techniques and best practices Threat: Ransomware Mitigations: Backups, IAM, Data Loss Prevention API Threats: Data misuse, privacy violations, sensitive/restricted/unacceptable content Mitigations: Classifying content using Cloud ML APIs; scanning and redacting data using Data Loss Prevention API Additional course details: Nexus Humans Security in Google Cloud training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Security in Google Cloud course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for experienced administrators with a background in either software development or system administration. Team leaders, lead developers, and other accidental Team Foundation Server administrators are also encouraged to take this course. This course will also help a student prepare for the relevant Microsoft certification exam. Overview At course completion, attendees will have had exposure to: - TFS editions and components - Supported topologies and environments - Must-have TFS administration tools - Hardware and software requirements - Required service accounts and permissions - Installing Team Foundation Server - Configuring Team Foundation Server - Managing relevant SQL Server components - Managing relevant SharePoint components - Installing and using Team Explorer - Team project collections and team projects - Using and customizing process templates - TFS licensing and Client Access Licenses - Connecting and using Microsoft Excel & Project - Installing and using Team Explorer Everywhere - Integrating TFS and SharePoint - Using the TFS web portal - Git and TFVC version control systems - Basic and advanced version control workflows - Configuring and using code search - Controlling access to version controlled items - Command-line and third party tools - Configuring automated builds - Deploying and using Visual Studio agents - Configuring agent pools and queues - Creating and queuing automated builds - Configuring Package Management - Configuring Release Management - Creating a release definition - Creating and deploying a release - Strategies for upgrading and migrating to TFS - Integrating TFS with other systems and data - High availability and scalability options - Capacity planning and disaster recovery - Backing up, restoring, and moving TFS data - Managing the data warehouse - Using PowerShell to manage TFS - Customizing Team Foundation Server - Extending Team Foundation Server Provides students with the knowledge and skills to deploy, configure, and manage Microsoft Team Foundation Server 2019 and related software components. Introduction to Team Foundation Server Introduction to Team Foundation Server Editions, components, and configurations Visual Studio Team Services comparison TFS' support of Application Lifecycle Management TFS administrator responsibilities and tasks ?Must-have? tools of a TFS administrator Planning and Deploying TFS Planning the deployment System requirements, software, and accounts Installing and configuring TFS Installing Team Explorer Troubleshooting Configuring TFS Administrator roles and tools Managing team project collections Managing team projects Managing process templates Securing TFS, SharePoint, and SQL Server Renaming and deleting a team project Client Applications TFS Client Access Licenses (CAL) Team Explorer and the web portal Microsoft Excel and Microsoft Project SharePoint project portal Team Explorer Everywhere Command-line and 3rd party tools Version Control Overview of Git and TFVC version control systems Integration with Visual Studio Using TFVC and Git version control Basic and advanced workflows Controlling access to version control Command-line tools and utilities TFS Proxy, MSSCCI Provider, and TFS Sidekicks Building and Releasing Overview of the Visual Studio build system Build agents, agent pools, agent queues Creating and queuing a build Monitoring, and managing a build Securing the build process Running tests as part of the build Overview of Package Management Overview of Release Management Defining, creating, and deploying a release Upgrading, Migrating, and Integrating Upgrading Team Foundation Server In-place vs. migration upgrade Performing post-upgrade tasks Migrating work items Migrating items under version controlled Integrating with Team Foundation Server Custom and 3rd party solutions Advanced Administration Monitoring the health of Team Foundation Server Web-based diagnostic tools Options for scalability and high availability Disaster recovery, backup, and restore Moving Team Foundation Server Managing the data warehouse Using PowerShell to manage TFS Customizing and Extending Customizing vs. extending Customizing a process template Customizing a work item type Creating default work items Creating and using a global list Using Witadmin.exe Using work item templates Creating a custom report Using the REST API to extend Team Foundation Server Additional course details: Nexus Humans Administering Team Foundation Server 2017 training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Administering Team Foundation Server 2017 course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 4 Days 24 CPD hours This course is intended for Application developers who want to increase their understanding of Spring and Spring Boot with hands-on experience and a focus on fundamentals Overview By the end of the course, you should be able to meet the following objectives: Spring configuration using Java Configuration and Annotations Aspect oriented programming with Spring Testing Spring applications using JUnit 5 Spring Data Access - JDBC, JPA and Spring Data Spring Transaction Management Simplifying application development with Spring Boot Spring Boot auto-configuration, starters and properties Build a simple REST application using Spring Boot, embedded Web Server and fat JARs or classic WARs Implementing REST client applications using RestTemplate and WebClient Spring Security Enable and extend metrics and monitoring capabilities using Spring Boot actuator Utilize Spring Boot enhancements to testing This course offers hands-on experience with the major features of Spring and Spring Boot, which includes configuration, data access, REST, AOP, auto-configuration, actuator, security, and Spring testing framework to build enterprise and microservices applications. On completion, participants will have a foundation for creating enterprise and cloud-ready applications. Introduction to Spring Java configuration and the Spring application context @Configuration and @Bean annotations @Import: working with multiple configuration files Defining bean scopes Launching a Spring Application and obtaining Beans Spring JAVA Configuration: A Deeper Look External properties & Property sources Environment abstraction Using bean profiles Spring Expression Language (SpEL) Annotation-based Dependency Injection Component scanning Autowiring using @Autowired Java configuration versus annotations, mixing. Lifecycle annotations: @PostConstruct and @PreDestroy Stereotypes and meta-annotations Factory Pattern in Spring Using Spring FactoryBeans Advanced Spring: How Does Spring Work Internally? The Spring Bean Lifecycle The BeanFactoryPostProcessor interception point The BeanPostProcessor interception point Spring Bean Proxies @Bean method return types Aspect-oriented programming What problems does AOP solve? Defining pointcut expressions Implementing various types of advice Testing a Spring-based Application Spring and Test-Driven Development Spring 5 integration testing with JUnit 5 Application context caching and the @Dirties Context annotation Profile selection with @Active Profiles Easy test data setup with @Sql Data Accss and JDBC with Spring How Spring integrates with existing data access technologies Data Access Exception hierarchy Spring?s Jdbc Template Database Transactions with Spring Transactions overview Transaction management with Spring Transaction propagation and rollback rules Transactions and integration testing Spring Boot Introduction Introduction to Spring Boot Features Value Proposition of Spring Boot Creating a simple Boot application using Spring Initializer website Spring Boot Dependencies, Auto-configuration, and Runtime Dependency management using Spring Boot starters How auto-configuration works Configuration properties Overriding auto-configuration Using Command Line Runner JPA with Spring and Spring Data Quick introduction to ORM with JPA Benefits of using Spring with JPA JPA configuration in Spring Configuring Spring JPA using Spring Boot Spring Data JPA dynamic repositories Spring MVC Architecture and Overview Introduction to Spring MVC and request processing Controller method signatures Using @Controller, @RestController and @GetMapping annotations Configuring Spring MVC with Spring Boot Spring Boot packaging options, JAR or WAR Rest with Spring MVC An introduction to the REST architectural style Controlling HTTP response codes with @ResponseStatus Implementing REST with Spring MVC, @RequestMapping, @RequestBody and @ResponseBody Spring MVC?s HttpMessageConverters and automatic content negotiation Spring Security What problems does Spring Security solve? Configuring authentication Implementing authorization by intercepting URLs Authorization at the Java method level Understanding the Spring Security filter chain Spring security testing Actuators, Metrics and Health Indicators Exposing Spring Boot Actuator endpoints Custom Metrics Health Indicators Creating custom Health Indicators External monitoring systems Spring Boot Testing Enhancements Spring Boot testing overview Integration testing using @SpringBootTest Web slice testing with MockMvc framework Slices to test different layers of the application Spring Security Oauth (Optional Topic) OAuth 2 Overview Implementing OAuth 2 using Spring Security OAuth Reactive Applications with Spring (Optional Topic) Overview of Reactive Programming concepts Reactive Programming support in Spring Using Spring?s reactive WebClient Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware Spring: Core Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the VMware Spring: Core Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 2 Days 12 CPD hours This course is intended for Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources. Overview Use Connector stages to read from and write to database tables Handle SQL errors in Connector stages Use Connector stages with multiple input links Use the File Connector stage to access Hadoop HDFS data Optimize jobs that write to database tables Use the Unstructured Data stage to extract data from Excel spreadsheets Use the Data Masking stage to mask sensitive data processed within a DataStage job Use the Hierarchical stage to parse, compose, and transform XML data Use the Schema Library Manager to import and manage XML schemas Use the Data Rules stage to validate fields of data within a DataStage job Create custom data rules for validating data Design a job that processes a star schema data warehouse with Type 1 and Type 2 slowly changing dimensions This course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course. Accessing databases Connector stage overview - Use Connector stages to read from and write to relational tables - Working with the Connector stage properties Connector stage functionality - Before / After SQL - Sparse lookups - Optimize insert/update performance Error handling in Connector stages - Reject links - Reject conditions Multiple input links - Designing jobs using Connector stages with multiple input links - Ordering records across multiple input links File Connector stage - Read and write data to Hadoop file systems Demonstration 1: Handling database errors Demonstration 2: Parallel jobs with multiple Connector input links Demonstration 3: Using the File Connector stage to read and write HDFS files Processing unstructured data Using the Unstructured Data stage in DataStage jobs - Extract data from an Excel spreadsheet - Specify a data range for data extraction in an Unstructured Data stage - Specify document properties for data extraction. Demonstration 1: Processing unstructured data Data masking Using the Data Masking stage in DataStage jobs - Data masking techniques - Data masking policies - Applying policies for masquerading context-aware data types - Applying policies for masquerading generic data types - Repeatable replacement - Using reference tables - Creating custom reference tables Demonstration 1: Data masking Using data rules Introduction to data rules - Using the Data Rules Editor - Selecting data rules - Binding data rule variables - Output link constraints - Adding statistics and attributes to the output information Use the Data Rules stage to valid foreign key references in source data Create custom data rules Demonstration 1: Using data rules Processing XML data Introduction to the Hierarchical stage - Hierarchical stage Assembly editor - Use the Schema Library Manager to import and manage XML schemas Composing XML data - Using the HJoin step to create parent-child relationships between input lists - Using the Composer step Writing Hierarchical data to a relational table Using the Regroup step Consuming XML data - Using the XML Parser step - Propagating columns Topic 6: Transforming XML data - Using the Aggregate step - Using the Sort step - Using the Switch step - Using the H-Pivot step Demonstration 1: Importing XML schemas Demonstration 2: Compose hierarchical data Demonstration 3: Consume hierarchical data Demonstration 4: Transform hierarchical data Updating a star schema database Surrogate keys - Design a job that creates and updates a surrogate key source key file from a dimension table Slowly Changing Dimensions (SCD) stage - Star schema databases - SCD stage Fast Path pages - Specifying purpose codes - Dimension update specification - Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions Demonstration 1: Build a parallel job that updates a star schema database with two dimensions Additional course details: Nexus Humans KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.