• Professional Development
  • Medicine & Nursing
  • Arts & Crafts
  • Health & Wellbeing
  • Personal Development

8437 Mode courses delivered Online

Microsoft SharePoint Modern Experience: Site Owner with Power Platform

By Nexus Human

Duration 1 Days 6 CPD hours This course is intended for This course is designed for existing Microsoft SharePoint site users who will create and manage SharePoint team or communication sites in SharePoint Online or SharePoint 2019. Overview In this course, you will create a SharePoint team site and a communication site, perform basic content management tasks on SharePoint sites, and configure workflows with Forms and Flow. You will: Create and configure new SharePoint sites. Create and configure document libraries. Create and configure lists. Assign permissions and access rights. Create and configure a communication site. Implement workflows with Forms and Flow. This course will empower you to use Microsoft© SharePoint© online to facilitate collaboration and allow you to use familiar applications and web-based tools to create, access, store, and track documents and data in a central location. Enable and configure SharePoint?s many distinct features and discover the many content structures that can be selected, added, and configured. Learn how to create, configure, and manage SharePoint sites so that your team or organization can share information and collaborate effectively.SharePoint content structures and configuration options are complex. As a site owner, it is critical that you understand what features, options, and content structures are available in SharePoint online, and how to properly configure them. With SharePoint sites, features, and content structures properly implemented, you will be able to securely share files, collaborate on documents, and access the information you need to work with your colleagues more effectively. Creating and Configuring a New Site Topic A: Create a Site in SharePoint Topic B: Configure Site Look and Layout Creating and Configuring Document Libraries Topic A: Create Document Libraries Topic B: Configure File Versioning and Checkout Topic C: Configure Content Approval Creating and Configuring Lists Topic A: Add List Apps to a Site Topic B: Create Custom Lists Topic C: Create Calculated and Validated Columns Assigning Permissions and Access Rights Topic A: Secure Sites with Permissions Topic B: Secure Apps, Documents, and Files Creating and Configuring a Communication Site Topic A: Create a Communication Site Topic B: Configure a Communication Site Implementing Workflows with Forms and Flow Topic A: Capture Data with Forms Topic B: Store Form Data in SharePoint with Flow Topic C: Automate Business Processes with Forms, Flow, and SharePoint

Microsoft SharePoint Modern Experience: Site Owner with Power Platform
Delivered OnlineFlexible Dates
Price on Enquiry

Advanced Coaching Course for Leaders

5.0(20)

By The Thinking Academy

This ilm recognised programme builds on the coaching fundamentals to focus on enhancing the coach’s self-awareness and coaching techniques, refining both practice and skill to develop a coaching style of leadership.

Advanced Coaching Course for Leaders
Delivered in Christchurch or UK Wide or OnlineFlexible Dates
Price on Enquiry

Danspirations - Dance Classes

5.0(3)

By Danspirations

Whether it's for fun, friendship, fitness, or for your future, dancing is a great way to express yourself and grow. If you're looking for something fun and free, or something more technical and focused there's a dance class here for you. We hope you will enjoy your time at our dance school! An exciting and fresh dance school in Aberdeen, Scotland.

Danspirations - Dance Classes
Delivered OnlineFlexible Dates
Price on Enquiry

M.D.D CHATGPT DATING ADVICE

4.9(27)

By Miss Date Doctor Dating Coach London, Couples Therapy

 MODERNIZED SERVICES Introduction: In the rapidly evolving realm of dating, finding reliable advice can be a daunting task. However, Miss Date Doctor is revolutionizing the industry by harnessing the power of artificial intelligence (AI) to provide contemporary, personalized dating services. Through the utilization of ChatGPT, Miss Date Doctor combines human expertise with cutting-edge AI technology, offering unrivaled support for individuals seeking dating advice. This article explores how ChatGPT’s AI-driven approach, integrated with Miss Date Doctor’s services, empowers customers with modernized strategies and a customized experience. https://relationshipsmdd.com/chatgpt-dating-advice/

M.D.D CHATGPT DATING ADVICE
Delivered in London or UK Wide or OnlineFlexible Dates
Price on Enquiry

WORKPLACE COUNSELLING SERVICES

4.9(27)

By Miss Date Doctor Dating Coach London, Couples Therapy

Miss Date Doctor workplace counselling services will provide services for your employees and support them with their relationship and life problems. We have a team of highly experienced and qualified professionals. The team is CPD, ICF, UKCP and BACP accredited. The M.D.D team consists of life coaches, counsellors and therapists. We offer three different packages for employers dependant on the size of your workforce. We are the most modern coaching and counselling platform in the UK. The workplace counselling services we offer have been tailored specifically to modern times and are very objective-driven and problem-solving based. We aim to give your employees the support they need whenever they need it. Call 03333443853 or email:enquiries@relationshipsmdd.com to get the solutions you need for your employees today. https://relationshipsmdd.com/workplace-counselling-services/

WORKPLACE COUNSELLING SERVICES
Delivered in London or UK Wide or OnlineFlexible Dates
Price on Enquiry

CertNexus Certified Artificial Intelligence Practitioner CAIP (AIP-210)

By Nexus Human

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

CertNexus Certified Artificial Intelligence Practitioner CAIP (AIP-210)
Delivered OnlineFlexible Dates
Price on Enquiry

Hands-on Predicitive Analytics with Python (TTPS4879)

By Nexus Human

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.

Hands-on Predicitive Analytics with Python (TTPS4879)
Delivered OnlineFlexible Dates
Price on Enquiry

CertNexus Certified Data Science Practitioner (CDSP)

By Nexus Human

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines

CertNexus Certified Data Science Practitioner (CDSP)
Delivered OnlineFlexible Dates
Price on Enquiry

0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25)

By Nexus Human

Duration 2 Days 12 CPD hours This course is intended for Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions. Overview Introduction to advanced statistical analysis Group variables: Factor Analysis and Principal Components Analysis Group similar cases: Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Predict categorical targets with Discriminant Analysis Predict categorical targets with Logistic Regression Predict categorical targets with Decision Trees Introduction to Survival Analysis Introduction to Generalized Linear Models Introduction to Linear Mixed Models This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. Introduction to advanced statistical analysis Taxonomy of models Overview of supervised models Overview of models to create natural groupings Group variables: Factor Analysis and Principal Components Analysis Factor Analysis basics Principal Components basics Assumptions of Factor Analysis Key issues in Factor Analysis Improve the interpretability Use Factor and component scores Group similar cases: Cluster Analysis Cluster Analysis basics Key issues in Cluster Analysis K-Means Cluster Analysis Assumptions of K-Means Cluster Analysis TwoStep Cluster Analysis Assumptions of TwoStep Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Nearest Neighbor Analysis basics Key issues in Nearest Neighbor Analysis Assess model fit Predict categorical targets with Discriminant Analysis Discriminant Analysis basics The Discriminant Analysis model Core concepts of Discriminant Analysis Classification of cases Assumptions of Discriminant Analysis Validate the solution Predict categorical targets with Logistic Regression Binary Logistic Regression basics The Binary Logistic Regression model Multinomial Logistic Regression basics Assumptions of Logistic Regression procedures Testing hypotheses Predict categorical targets with Decision Trees Decision Trees basics Validate the solution Explore CHAID Explore CRT Comparing Decision Trees methods Introduction to Survival Analysis Survival Analysis basics Kaplan-Meier Analysis Assumptions of Kaplan-Meier Analysis Cox Regression Assumptions of Cox Regression Introduction to Generalized Linear Models Generalized Linear Models basics Available distributions Available link functions Introduction to Linear Mixed Models Linear Mixed Models basics Hierachical Linear Models Modeling strategy Assumptions of Linear Mixed Models Additional course details: Nexus Humans 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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.

0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25)
Delivered OnlineFlexible Dates
Price on Enquiry

Business Analysis - Foundation

By Nexus Human

Duration 3 Days 18 CPD hours This course is intended for The Foundation course is designed for individuals who want to gain an overview of Business Analysis (Business Analysts, Requirements Engineers, Product manager, Product Owner, Chief Product Owner, Service Manager, Service Owner, Project manager, Consultants) Overview Students should be able to demonstrate knowledge and understanding of business analysis principles and techniques. Key areas are: the role and competencies of a business analyst strategy analysis business system and business process modelling stakeholder analysis investigation and modelling techniques requirements engineering business case development The business analyst role analyzes, understands and manages the requirements in a customer-supplier relationship and ensures that the right products are delivered. The Foundation Seminar gives a good introduction to the spectrum of this responsibility. Course Introduction Let?s Get to Know Each Other Course Overview Course Learning Objectives Course Structure Course Agenda Introduction to Business Analysis Structure and Benefits of Business Analysis Foundation Exam Details Business Analysis Certification Scheme What is Business Analysis? Intent and Context Origins of business analysis The development of business analysis The scope of business analysis work Taking a holistic approach The role and responsibilities of the business analyst The competencies of a Business Analyst Personal qualities Business knowledge Professional techniques The development of competencies Strategy Analysis The context for strategy The defiition of strategy Strategy development External environmental analysis Internal invironmental analysis SWOT analysis Executing strategy Business Analysis Process Model An approach to problem solving Stages of the business analysis process model Objectives of the process model stages Procedures for each process model stage Techniques used within each process model stage Investigation Techniques Interviews Observation Workshops Scenarios Prototyping Quantitative approaches Documenting the current situation Stakeholder Analysis and Management Stakeholder categories and identification Analysing stakeholders Stakeholder management strategies Managing stakeholders Understanding stakeholder perspectives Business activity models Modelling Business Processes Organizational context An altrnative view of an organization The organizational view of business processes Value propositions Process models Analysing the as-is process model Improving business processes (to-be business process) Defining the Solution Gab analysis Introduction to Business Architecture Definition to Business Architecture Business Architecture techniques Business and Financial Case The business case in the project lifecycle Identifying options Assessing project feasibility Structure of a business case Investment appraisal Establishing the Requirements A framework for requirements engineering Actors in requirements engineering Requirements elicitation Requirements analysis Requirements validation Documenting and Managing the Requirements The requirements document The requirements catalogue Managing requirements Modelling the Requirements Modelling system functions Modelling system data Delivering the Requirements Delivering the solution Context Lifecycles Delivering the Business Solution BA role in the business change lifecycle Design stage Implementation stage Realization stage

Business Analysis - Foundation
Delivered OnlineFlexible Dates
Price on Enquiry