42810 Learning courses delivered Online

Person-centred approaches are a core skills framework that articulates what it means to be person-centred and how to develop and support the workforce to work in this way. Developed in partnership with Skills for Health and Skills for Care, the Framework aims to distil best practices and to set out core, transferable behaviours, knowledge and skills. It is applicable across services and sectors and across different types of organisations. Person-centred approaches underpins existing dementia, learning disabilities, mental health and end of life care core skills frameworks. This subject forms standard 5 in The Care Certificate.

Move up a Grade A Revision guide for students in all subjects at school, university and professional levels Course Information   Description Exams are a difficult time. Studying can be tedious and frustrating, but you have to put in the hours in to get the marks. There are no quick fixes, but you can get a better grade with a good revision strategy. This course presents the revision strategy I used to get the second highest mark in the finals of my accountancy qualifications and 10 A’s out of 11 exams through three years of accountancy study. The revision strategy is not complicated, and you still have to do the work – it’s not a magic solution – but it guides you through each phase of revision, and the exam itself, to help you get the best possible marks. I firmly believe that this revision strategy will get you a better mark than you would have without it. Thank you for your interest in this course. I hope this course will help you prepare for your exams   Course Pre-Requisites There are no pre-course requirements.   What Students will Learn How to structure your days and weeks during the revision period How to plan your revision An effective way to learn your material The importance of giving your mind a break – every day The importance of exam practice How to allocate the exam time over the questions How exam marks work To go for the marks, rather than for perfection What to do (and what not to do) on the day of the exam Tips for the exam A simple technique to reduce stress and boost learning     Curriculum SS1 Introduction and Agenda 3 mins SS2 Structure your Week 7 mins SS3 Start Each Day on a Positive Note 2 mins SS4 Revision Phase 1 2 mins SS5 Revision Phase 2 2 mins SS6 Give your Mind a Break 2 mins SS7 Revision Phase 3 5 mins SS8 Revision Phase 4 2 mins SS9 Exam Preparation 1 2 mins SS10 Exam Preparation 2 5 mins SS11 Emotional Freedom Technique 9 mins SS12 The Exam 5 mins SS13 Final Thoughts on Revision 6 mins SS14 Test your Knowledge 8 mins   Total time 1 hour    Additional Resources None     Course Tutor Your tutor is Ross Maynard. Ross is a Fellow of the Chartered Institute of Management Accountants in the UK and has 30 years’ experience as a process improvement consultant specialising in finance processes. Ross is also a professional author of online training courses for accountants. Ross lives in Scotland with his wife, daughter and Cocker Spaniel

An instructor-led leadership learning programme based on emotional intelligence and social neuroscience, designed to boost leadership 'PowerSkills.' A practical programme that provides leaders with a learning journey that equips them with the tools and techniques to connect, empathise, communicate effectively, build employee engagement and influence.

Developing a solid foundation in Greek grammar will help you create your own sentences correctly and will also make it easier to improve your communication skills in both spoken and written Greek. So this course has been designed to help you steadily advance with the Greek language. Here, on the Greek Online School Learning Management System (LMS) you will find all the grammar phenomena that you need to know for the A2 Level (basic knowledge) in Greek, the language that influenced all European languages.

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.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist 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 Cloudera Data Scientist 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 1 Days 6 CPD hours This course is intended for This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam. Overview This course teaches participants the following skills: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum. Understanding the Professional Data Engineer Certification Position the Professional Data Engineer certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Data Engineer and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies for the Professional Data Engineer Exam Flowlogistic MJTelco Designing and Building (Review and preparation tips) Designing data processing systems Designing flexible data representations Designing data pipelines Designing data processing infrastructure Build and maintain data structures and databases Building and maintaining flexible data representations Building and maintaining pipelines Building and maintaining processing infrastructure Analyzing and Modeling (Review and preparation tips) Analyze data and enable machine learning Analyzing data Machine learning Machine learning model deployment Model business processes for analysis and optimization Mapping business requirements to data representations Optimizing data representations, data infrastructure performance and cost Reliability, Policy, and Security (Review and preparation tips) Design for reliability Performing quality control Assessing, troubleshooting, and improving data representation and data processing infrastructure Recovering data Visualize data and advocate policy Building (or selecting) data visualization and reporting tools Advocating policies and publishing data and reports Design for security and compliance Designing secure data infrastructure and processes Designing for legal compliance Resources and next steps Resources for learning more about designing data processing systems, data structures, and databases Resources for learning more about data analysis, machine learning, business process analysis, and optimization Resources for learning more about data visualization and policy Resources for learning more about reliability design Resources for learning more about business process analysis and optimization Resources for learning more about reliability, policies, security, and compliance Additional course details: Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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 1 Days 6 CPD hours This course is intended for This seminar is intended for individuals who want to gain intermediate knowledge of Sales. Overview Upon successful completion of this seminar, guests will gain intermediate knowledge of Sales Leadership and learning resource availability. In this seminar, guests will obtain knowledge in Sales Leadership, leveraging New Horizons' Leadership and Professional Development Program. Sales Leadership Session Sales Leadership Topics

Duration 2 Days 12 CPD hours This course is intended for Built for experienced IT Professionalsworking with Citrix Virtual Appsand Desktops who need to plan for,implement, or manage a ProvisioningServices environment. Potential studentsinclude administrators, engineers, andarchitects. Overview #NAME? In this course, students will learn to install and configure a highly available Citrix Provisioning farm according to leading practices. In this course, students will learn about the architecture, communication, and processes that make up Citrix Provisioning to be successful with deploying and managing a farm. Manage and integrate vDisks and target devices with Citrix Virtual Apps and Desktops for easy rollback, upgrades, and performance of Virtual Delivery Agent machines. At the end of this course students will be able to install, configure and manage the CitrixProvisioning 7 solution. Advanced Provisioning Learning Objectives Introduction to Citrix Provisioning (PVS) Getting Started with Citrix Provisioning Citrix Provisioning Architecture Citrix Provisioning Infrastructure Lab VM Power Management Learning Objectives The Citrix Provisioning Server The Farm Database The Store Streaming the vDisk Lab VM Power Management Learning Objectives vDisk Introduction Master Target Device Preparation Streaming Introduction Boot Methods Target Devices Lab VM Power Management Learning Objectives Target Devices Introduction Reads and Writes Machine and User Data Integrating Citrix Provisioning with Citrix Virtual Apps and Desktops Lab VM Power Management Learning Objectives The Complete Architecture Overview The Citrix Virtual Desktops Setup Wizard Manage the Target Devices through Creating Device Collections Using Provisioned Services with Citrix Virtual Apps and Desktops Managing Citrix Provisioning from Citrix Cloud Advanced Architecture Lab VM Power Management Learning Objectives Farm Component Scalability Store Redundancy Farm Database Redundancy Supporting Citrix Provisioning Lab VM Power Management Learning Objectives vDisk Updates Delegate Administration Audit and Support Alternative vDisk Update Methods

Fostering a growth mindset in education transforms assessments into tools for learning, encouraging resilience, effort, and continuous improvement beyond traditional grading for enhanced student development.