159 Library courses in Coventry delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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 Data Warehouse AdministratorDatabase Administrators Overview Use the Oracle Database tuning methodology appropriate to the available toolsUtilize database advisors to proactively tune an Oracle Database InstanceUse the tools based on the Automatic Workload Repository to tune the databaseDiagnose and tune common SQL related performance problemsDiagnose and tune common Instance related performance problemsUse Enterprise Manager performance-related pages to monitor an Oracle DatabaseGain an understanding of the Oracle Database Cloud Service In the Oracle Database 12c: Performance Management and Tuning course, learn about the performance analysis and tuning tasks expected of a DBA: proactive management through built-in performance analysis features and tools, diagnosis and tuning of the Oracle Database instance components, and diagnosis and tuning of SQL-related performance issues. In this course, you will be introduced to Oracle Database Cloud Service. Introduction Course Objectives Course Organization Course Agenda Topics Not Included in the Course Who Tunes? What Does the DBA Tune? How to Tune Tuning Methodology Basic Tuning Diagnostics Performance Tuning Diagnostics Performance Tuning Tools Tuning Objectives Top Timed Events DB Time CPU and Wait Time Tuning Dimensions Time Model Dynamic Performance Views Using Automatic Workload Repository Automatic Workload Repository Overview Automatic Workload Repository Data Enterprise Manager Cloud Control and AWR Snapshots Reports Compare Periods Defining the Scope of Performance Issues Defining the Problem Limiting the Scope Setting the Priority Top SQL Reports Common Tuning Problems Tuning During the Life Cycle ADDM Tuning Session Performance Versus Business Requirements Using Metrics and Alerts Metrics and Alerts Overview Limitation of Base Statistics Benefits of Metrics Viewing Metric History Information Viewing Histograms Server-Generated Alerts Setting Thresholds Metrics and Alerts Views Using Baselines Comparative Performance Analysis with AWR Baselines Automatic Workload Repository Baselines Moving Window Baseline Baselines in Performance Page Settings Baseline Templates AWR Baseslines Creating AWR Baselines Managing Baselines with PL/SQL Using AWR-Based Tools Automatic Maintenance Tasks ADDM Performance Monitoring Using Compare Periods ADDM Active Session History New or Enhanced Automatic Workload Repository Views Emergency Monitoring Real-time ADDM Real-Time Database Operation Monitoring Overview Use Cases Defining a Database Operation Scope of a Composite Database Operation Database Operation Concepts Identifying a Database Operation Enabling Monitoring of Database Operations Identifying, Starting, and Completing a Database Operation Monitoring Applications What is a Service? Service Attributes Service Types Creating Services Managing Services in a Single-Instance Environment Where are Services Used? Using Services with Client Applications Services and Pluggable Databases Identifying Problem SQL Statements SQL Statement Processing Phases Role of the Oracle Optimizer Identifying Bad SQL Top SQL Reports SQL Monitoring What is an Execution Plan? Methods for Viewing Execution Plans Uses of Execution Plans Influencing the Optimizer Functions of the Query Optimizer Selectivity Cardinality and Cost Changing Optimizer Behavior Optimizer Statistics Extended Statistics Controlling the Behavior of the Optimizer with Parameters Enabling Query Optimizer Features Reducing the Cost of SQL Operations Reducing the Cost Index Maintenance SQL Access Advisor Table Maintenance for Performance Table Reorganization Methods Space Management Extent Management Data Storage Using SQL Performance Analyzer Real Application Testing: Overview Real Application Testing: Use Cases SQL Performance Analyzer: Process Capturing the SQL Workload Creating a SQL Performance Analyzer Task SQL Performance Analyzer: Tasks Parameter Change SQL Performance Analyzer Task Page SQL Performance Management Maintaining SQL Performance Maintaining Optimizer Statistics Automated Maintenance Tasks Statistic Gathering Options Setting Statistic Preferences Restore Statistics Deferred Statistics Publishing Automatic SQL Tuning Using Database Replay Using Database Replay The Big Picture System Architecture Capture Considerations Replay Considerations: Preparation Replay Considerations Replay Options Replay Analysis Tuning the Shared Pool Shared Pool Architecture Shared Pool Operation The Library Cache Latch and Mutex Diagnostic Tools for Tuning the Shared Pool Avoiding Hard Parses Reducing the Cost of Soft Parses Sizing the Shared Pool Tuning the Buffer Cache Oracle Database Architecture: Buffer Cache Buffer Cache: Highlights Database Buffers Buffer Hash Table for Lookups Working Sets Buffer Cache Tuning Goals and Techniques Buffer Cache Performance Symptoms Buffer Cache Performance Solutions Tuning PGA and Temporary Space SQL Memory Usage Performance Impact Automatic PGA Memory SQL Memory Manager Configuring Automatic PGA Memory Setting PGA_AGGREGATE_TARGET Initially Limiting the size of the Program Global Area (PGA) SQL Memory Usage Automatic Memory Oracle Database Architecture Dynamic SGA Granule Memory Advisories Manually Adding Granules to Components Increasing the Size of an SGA Component Automatic Shared Memory Management: Overview SGA Sizing Parameters: Overview Performance Tuning Summary with Waits Commonly Observed Wait Events Additional Statistics Top 10 Mistakes Found in Customer Systems Symptoms Oracle Database Cloud Service: Overview Database as a Service Architecture, Features and Tooling Software Editions: Included Database Options and Management Packs Accessing the Oracle Database Cloud Service Console Automated Database Provisioning Managing the Compute Node Associated With a Database Deployment Managing Network Access to Database as a Service Scaling a Database Deployment Performance Management in the Database Cloud Environment Performance Monitoring and Tuning What Can be Tuned in a DBCS Environment?

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.

Join the Historical Association and the Royal Geographical Society at this special online twilight forum event specifically for primary teachers of history and geography. The theme this time will be climate education and how primary teachers can develop this through their history and geography lessons. With a keynote speaker and workshop sessions from Ailsa Fidler and Emma Espley, plus an opportunity to network and share ideas, this event will support primary teachers to better understand how history and geography can feed into the responsibility of every curriculum subject to educate our young people about the climate crisis and sustainable futures.

Duration 5 Days 30 CPD hours This course is intended for This course is recommended for administrators and engineers. Overview What you'll learn: Understand the differences between Citrix Virtual Apps and Desktops 2203 LTSR on-premises and the Citrix DaaS. Install, configure, and manage Citrix Cloud Connectors. Deploy and manage Virtual Delivery Agent machines to on-premises resource locations as well as in Microsoft Azure using MCS. Integrate Citrix Cloud and Citrix Virtual Apps and Desktops 2203 LTSR with Microsoft Azure Active Directory. Provide remote access with Citrix StoreFront and Citrix Gateway on Microsoft Azure. In this course you will learn how to create a new Citrix DaaS deployment on Citrix Cloud, with a resource location on Microsoft Azure. You will also learn how to migrate to Citrix DaaS from an on-premises Citrix Virtual Apps and Desktops Site. Get hands-on as the course guides you through the architecture, communications, management, installation, and configuration of Citrix DaaS on Citrix Cloud and a Microsoft Azure resource location that will host apps and desktops for your users. This course is a necessary step in enabling you with the right training and skills, to not only understand, manage, and deliver successfully, but also to make well-informed planning decisions along the way. Module 1: Introduction to Citrix DaaS New Citrix Workspace Packaging Citrix Virtual Apps and Desktops - On-Premises Site What is Citrix Cloud? Why Citrix DaaS? What is a Migration from Citrix Virtual Apps and Desktops to Citrix DaaS? Citrix Cloud Administration Module 2: Planning - Citrix DaaS Architecture, Security, and Operations Architecture and Deployment Options Citrix DaaS Security Citrix DaaS Operations Module 3: Planning - Citrix Cloud Connectors Cloud Connector Architecture Cloud Connector Services and Communications Overview Cloud Connector Operations in a Resource Location Cloud Connector Resiliency Installing, Updating, and Removing Cloud Connectors Supported Domain Scenarios for Cloud Connectors Securing Cloud Connector Communications Local Host Cache (LHC) Citrix Cloud Connector vs Delivery Controller Operations Module 4: Planning - Citrix DaaS Resource Locations Citrix DaaS Resource Locations Citrix DaaS Hosting Connections Zones Module 5: Active Directory, Authentication, and Authorization Active Directory Design Options Desktops from Non-Domain Joined VDAs Citrix Federated Authentication Service and Identity Provider Services Module 6: Planning - Provisioning VDA Workloads and Delivering Resources Master Images Machine Creation Services (MCS) in Citrix DaaS Citrix Provisioning in Citrix DaaS Machine Catalogs Delivery Groups Citrix Cloud Library Module 7: Planning - Provide Access in Citrix Cloud Selecting Between Citrix digital workspace experience and StoreFront Citrix StoreFront and Citrix digital workspace experience Communications Selecting Between Citrix Gateway Service and On-Premises Citrix Gateway Access Layer Communications User Authentication Module 8: Planning - Citrix DaaS Administration Citrix Cloud Manage and Monitor Delegated Administration Citrix DaaS Remote PowerShell Software Development Kit Manage Multiple Resource Locations Module 9: Planning - Public Cloud Considerations General Public Cloud Considerations Using Autoscale to Power Manage Machines in a Public Cloud Microsoft Azure as a Citrix DaaS Resource Location Amazon Web Services as a Citrix DaaS Resource Location Google Cloud as a Citrix DaaS Resource Location Module 10: Planning - Migrating to Citrix DaaS from Citrix Virtual Apps and Desktops Citrix Cloud Migration Options and Considerations Citrix Automated Configuration Tool Citrix Image Portability Service Module 11: Manage - Operations and Support in Citrix Cloud Citrix Cloud Connector Support Updating and Rolling Back Machine Catalogs VDA Restore Citrix Self-Help Strategy Monitor Your Environment Module 12: Introduction to Citrix DaaS on Microsoft Azure Partnering for Success Module 13: Planning - Citrix DaaS Resource Location on Microsoft Azure Overview of Citrix DaaS Components Creating a Citrix DaaS Deployment Overview Module 14: Planning - Microsoft Azure Overview Azure Virtual Network Structure Azure Virtual Network Connectivity Azure Virtual Resources Azure Active Directory Identity and Access Management Azure Active Directory Options and Considerations Module 15: Planning - Deploying Citrix DaaS on Microsoft Azure Citrix DaaS Resource Locations in Azure Citrix DaaS Components in Azure Creating and Managing Workloads in an Azure Resource Location Module 16: Planning - Provide Access to End Users Providing Access to Resources in Citrix Cloud Citrix Gateway Deployment Options Deploying Citrix Gateway or ADC in Azure GSLB and StoreFront Optimal Gateway in Hybrid Environments Module 17: Rollout - Citrix DaaS Deployment on Microsoft Azure Citrix Workspace App Rollout Preparing Migration of End-Users to Workspace Platform Module 18: Managing - Citrix DaaS Workloads on Microsoft Azure Maintaining Citrix Gateway Backup and Monitoring in Azure Maintaining Master Images in Azure Monitoring VDAs in Manage Console and Azure Module 19: Optimize - Citrix DaaS on Microsoft Azure Managing Azure Costs Using Azure Pricing Calculator - Instructor Demo Additional course details: Nexus Humans CWS-252 Citrix DaaS Deployment and Administration on Microsoft Azure 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 CWS-252 Citrix DaaS Deployment and Administration on Microsoft Azure 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 introductory-level, fast-paced course is for skilled web developers new to React who have prior experienced working HTML5, CSS3 and JavaScript. Overview Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore: A basic and advanced understanding of React components An advanced, in-depth knowledge of how React works A complete understanding of using Redux How to build, validate, and populate interactive forms How to use inline styles for perfect looking components How to test React components How to build and use components How to get control of your build process A deep understanding of data-driven modeling with props and state How to use client-side routing for pages in your apps How to debug a React application Mastering React is a comprehensive hands-on course that aims to be the single most useful resource on getting up to speed quickly with React. Geared for more experienced web developers new to React, this course provides students with the core knowledge and hands-on skills they require to build reliable, powerful React apps. After the first few modules, you?ll have a solid understanding of React?s fundamentals and will be able to build a wide array of rich, interactive web apps with the framework. The first module is an introduction to the new functionality in ECMAScript 6 (JavaScript). Client-side routing between pages, managing complex state, and heavy API interaction at scale are also covered. This course consists of two parts. In the first part of the course students will explore all the fundamentals with a progressive, example-driven approach. You?ll create your first apps, learn how to write components, start handling user interaction, and manage rich forms. We end the first part by exploring the inner workings of Create React App (Facebook?s tool for running React apps), writing automated unit tests, and building a multi-page app that uses client-side routing. The latter part of the course moves into more advanced concepts that you?ll see used in large, production applications. These concepts explore strategies for data architecture, transport, and management: Redux is a state management paradigm based on the Flux architecture. Redux provides a structure for large state trees and allows you to decouple user interaction in your app from state changes. GraphQL is a powerful, typed, REST API alternative where the client describes the data it needs. Hooks is the powerful, new way to maintain state and properties with functional components and the future of React according to Facebook. ES6 Primer (Optional) Prefer const and let over var Arrow functions Modules Object.assign() Template literals The spread operator and Rest parameters Enhanced object literals Default arguments Destructuring assignments Your first React Web Application Setting up your development environment JavaScript ES6 /ES7 Getting started What?s a component? Our first component Building the App Making the App data-driven Your app?s first interaction Updating state and immutability Refactoring with the Babel plugin transform-class-properties JSX and the Virtual DOM React Uses a Virtual DOM Why Not Modify the Actual DOM? What is a Virtual DOM? Virtual DOM Pieces ReactElement JSX JSX Creates Elements JSX Attribute Expressions JSX Conditional Child Expressions JSX Boolean Attributes JSX Comments JSX Spread Syntax JSX Gotchas JSX Summary Components A time-logging app Getting started Breaking the app into components The steps for building React apps from scratch Updating timers Deleting timers Adding timing functionality Add start and stop functionality Methodology review Advanced Component Configuration with props, state, and children ReactComponent props are the parameters PropTypes Default props with getDefaultProps() context state Stateless Components Talking to Children Components with props.children Forms Forms 101 Text Input Remote Data Async Persistence Redux Form Modules Unit Testing & Jest Writing tests without a framework What is Jest? Using Jest Testing strategies for React applications Testing a basic React component with Enzyme Writing tests for the food lookup app Writing FoodSearch.test.js Routing What?s in a URL? React Router?s core components Building the components of react-router Dynamic routing with React Router Supporting authenticated routes Intro to Flux and Redux Why Flux? Flux is a Design Pattern Flux implementations Redux & Redux?s key ideas Building a counter The core of Redux The beginnings of a chat app Building the reducer() Subscribing to the store Connecting Redux to React Intermediate Redux Using createStore() from the redux library Representing messages as objects in state Introducing threads Adding the ThreadTabs component Supporting threads in the reducer Adding the action OPEN_THREAD Breaking up the reducer function Adding messagesReducer() Defining the initial state in the reducers Using combineReducers() from redux React Hooks Motivation behind Hooks How Hooks Map to Component Classes Using Hooks Requires react 'next' useState() Hook Example useEffect() Hook Example useContext() Hook Example Using Custom Hooks Using Webpack with Create React App JavaScript modules Create React App Exploring Create React App Webpack basics Making modifications Hot reloading; Auto-reloading Creating a production build Ejecting Using Create React App with an API server When to use Webpack/Create React App Using GraphQL Your First GraphQL Query GraphQL Benefits GraphQL vs. REST GraphQL vs. SQL Relay and GraphQL Frameworks Chapter Preview Consuming GraphQL Exploring With GraphiQL GraphQL Syntax 101 . Complex Types Exploring a Graph Graph Nodes ; Viewer Graph Connections and Edges Mutations Subscriptions GraphQL With JavaScript GraphQL With React

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

Duration 5 Days 30 CPD hours This course is intended for This course is recommended for: ? System Administrators ? Patch Administrators ? SA Administrators ? Policy Setters ? IT Managers ? IT or Application Architects ? Data Center Managers ? Application Experts ? Operations Experts ? Deployment Specialists ? Application Deployment Manager Administrators ? QA Team Members and Managers ? Security Administrators ? Other technical personnel who are responsible for data center automation Overview After completing this course, you should be able to: ? Describe the Server Automation (SA) core architecture and key components of SA ? Integrate unmanaged servers into the SA managed environment and discover server information through SA ? Use the Operation System (OS) Provisioning feature of SA to automate the installation of OS onto servers ? Provision virtual servers and manage their server lifecycle through SA ? Create static and dynamic device groups to manage multiple servers as groups ? Manage packages as part of the software management feature in SA ? Use a policy-based management methodology to provision software and manage software updates ? Describe and use Application Deployment Manager (ADM) to manage an application lifecycle using SA ? Use the Application Configuration feature of SA to manage the values in configuration files on managed servers ? Manage patches on various OS platforms using a patch policy or software policy ? Use the Audit and Remediation feature of SA to ensure servers and applications are compliant with defined best practices or corporate policies ? Use the Compliance feature of SA to discover out-of-compliance servers in the managed environment and ensure that they are in compliance with corporate policies ? Use the Global Shell and Global File System (OGFS) features to explore and manage servers in an SA environment ? Create, manage, and execute scripts I SA to manage servers or server groups This five-day course provides the necessary foundation to manage the data center servers and application environment using HP Server Automation (SA) 10. The course covers the key components of SA and their functionality. Course Overview and Introduction to HP Server Automation ? Discuss the IT organization?s preference for automation ? Discuss the main features of HP SA ? Discuss the benefits of using SA ? Describe the distributed architecture of SA Exploring the Architecture and Interfaces ? Define an SA core ? Describe the functionality of each key component of SA ? Describe SA users ? Use the SA client to explore a managed server environment ? Describe the SA core architecture and key components ? Learn how to use the SA interfaces to explore the server environment Agent Functionality and Server Integration ? Differentiate the agent types ? Describe the SA agent functionality ? Specify the requirements for installing an SA agent onto an unmanaged server ? Install an agent onto an unmanaged server using the SA client and manual installation ? Troubleshoot agent installation and communication failures ? Explore the server inventory using the Device Explorer of the SA client ? Describe the Agent Tools feature within SA ? Integrate existing, unmanaged servers into the SA managed environment ? Summarize what server information is collected by the agent ? Explain the server module objects ? Explain agent extensions Provisioning Operating Systems (OS) Using OS Build Plans (OSBPs) ? Describe the Operating System (OS) provisioning feature within SA ? Describe the process of provisioning an OS on a server ? Define and run an OS Build Plan (OSBP) Dynamic Host Configuration Protocol (DHCP) Less or Static IP Provisioning ? Configure and run the Managed Boot Client(s) (MBC) ? Provision Operating Systems (OS) without using Dynamic Host Configuration Protocol (DHCP) (or by using Static IP) Managing Virtualization ? Describe the supported virtualization management features in SA ? Describe the process of provisioning virtual servers for VMware Enterprise Server Xi (VMware ESXi) and Solaris 10 ? Identify the relationship between the hypervisor and its virtual servers ? Manage the lifecycle of VMware Virtual Machines (VMs) ? Manage the lifecycle of Solaris zones ? Integrate with VMware Virtual Center using Virtualization Service (VS) Exploring Device Groups ? Explain device groups and their characteristics ? Describe the different types of device groups supported in SA ? Create static and dynamic device groups using the SA client Exploring Device Groups with Search Results ? Describe the different types of search in the SA client ? Discuss the process to save and retrieve search results ? Create device groups from search results ? Develop sample reports using advanced search Managing Packages ? Describe how to manage packages in SA ? Discuss the supported package types ? Explain how to organize the software library ? Import and export packages into the software repository ? Install and uninstall packages ? Manage Red Hat Package Manager (RPM) packages Software Management ? Describe the use of policy-based software management in SA ? Describe how to manage software policies in SA ? List the software management setup tasks ? Install and uninstall software using software policies ? Manage software updates using software policies Working with Application Deployment Manager (ADM) ? Describe the Application Deployment Manager (ADM) and its functions ? Manage the ADM ? Set permissions for ADM ? Define an application, a target, and a component ? Deploy an application ? Manage an Application Deployment job ? Describe the rollback and undeploy process ? Import and export Application Deployment data from SA Application Configuration Management ? Describe how application configurations are managed in SA ? Describe application configuration components ? Control values using an application configuration inheritance model ? Push application configuration values to servers Managing Patches ? Describe the patch management feature in SA ? View patch information ? Describe UNIX patch management tasks ? Install patches using patch policies on the Windows platform ? Identify Microsoft patch administration tasks ? Manage patches on Red Hat LINUX Working with Audits, Snapshots, and Remediation ? Describe the audit and remediation feature in SA ? Create and run audits ? Configure file audit rules ? Set audit rule exceptions ? View audit results and remediate the differences ? Describe how to use Business Service Automation Essentials (BSAE) Network to run compliance audits Enforcing Compliance ? Define server compliance concepts ? Describe the compliance management feature in SA ? Scan and view the compliance status of servers ? Remediate non-compliant servers Exploring Servers Using the Global Shell and Global File System ? Describe the Global Shell and Opsware Global File System (OGFS) features within SA ? Describe how Global Shell and OGFS features can be used to manage servers within the SA environment ? Describe how to navigate and filter data in the OGFS using the Global Shell ? Use the Remote Shell (ROSH) command to login to a managed server and execute shell scripts on a managed server ? Use the SA remote terminal feature to access and manage servers in the managed environment Scripting with SA ? Describe the script management and execution feature in SA ? Create scripts using the SA client ? Execute ad hoc or saved scripts ? View and download script results ? Describe PowerShell integration with SA ? Explain the Extensible Discovery server module ? Introduce Automation Platform eXtensions (APX) scripting Exploring Reports in SA ? Explain SA reports ? List the reports available in SA ? Generate an SA report ? Explain Business Service Automation (BSA) Essentials basics as a reporting tool OS Provisioning with OS Sequences ? Describe the OS Provisioning feature within SA ? Describe the process of provisioning an OS on a server ? Define and run an OS Sequence

Course Overview The "Adobe Lightroom CC" course offers learners an in-depth understanding of the powerful photo editing and organising tools within Adobe Lightroom CC. This course is designed to help individuals at all levels develop proficiency in managing and editing their images efficiently. It covers both the Lightroom Classic CC and Lightroom CC applications, offering learners insights into non-destructive editing techniques, file management, and advanced photo adjustments. Upon completion, learners will be equipped to enhance their photography workflow, ensuring that they can bring their creative visions to life with ease. Course Description This comprehensive course covers key areas of Adobe Lightroom CC, including the differences between Lightroom Classic CC and Lightroom CC, and their respective strengths. Learners will explore how to organise and edit photos using a variety of features such as editing tools, presets, and colour corrections. Additionally, learners will gain knowledge on how to submit photos efficiently within Lightroom and learn best practices for organising a photo library. Throughout the course, participants will develop the skills necessary to refine their editing abilities, increase productivity, and optimise their overall workflow in a professional photography environment. Course Modules Module 01: Lightroom Classic CC Module 02: Lightroom CC Module 03: Photo Submission (See full curriculum) Who is this course for? Individuals seeking to enhance their photo editing skills. Professionals aiming to streamline their photo management and editing processes. Beginners with an interest in photography and photo editing. Photography enthusiasts wanting to optimise their Lightroom workflow. Career Path Professional Photographer Photo Editor Graphic Designer Digital Imaging Specialist Content Creator Photography Studio Assistant