2499 Evaluation courses in London delivered Online

Effecting Business Process Improvement: In-House Training Business analysts facilitate the solution of business problems. The solutions are put into practice as changes to the way people perform in their organizations and the tools they use. The business analyst is a change agent who must understand the basic principles of quality management. This course covers the key role that business analysts play in organizational change management. What you will Learn You will learn how to: Define and document a business process Work with various business modeling techniques Perform an enterprise analysis in preparation for determining requirements Analyze business processes to discern problems Foundation Concepts Overview of business analysis and process improvement Defining the business process Introducing the proactive business analyst Focusing on business process improvement for business analysts Launching a Successful Business Process Improvement Project Overview of the launch phase Understanding and creating organizational strategy Selecting the target process Aligning the business process improvement project's goals and objectives with organizational strategy Defining the Current Process Overview of current process phase Documenting the business process Business modeling options: work-flow models Business modeling options: Unified Modeling Language (UML) model adaptations for business processes Analyzing the Current Process Process analysis overview Evaluation: establishing the control group Opportunity techniques: multi-discipline problem-solving Opportunity techniques: matrices Building and Sustaining a Recommended Process Overview of the recommended process and beyond Impact analysis Recommended process Transition to the business case Return to proactive state

Most trainers are self-taught or have attended short courses and don't have the time to take their skills to the next level. Give staff the skills to understand how and what to train, and to use our special learner-centred techniques to boost the effectiveness and impact of their training courses.. This course includes: Assessing your learners’ skills and abilities Understanding how people learn Learning and training theories Planning and timing an effective session Essential training techniques Assessment and evaluation of learning

Our online Focus Awards Level 5 Diploma in Education & Training (RQF) course will provide you with a strong background in understanding the skills you need to progress in the workplace and earn a recognised qualification.

Course Description:These two days are dedicated to nurses and other allied healthcare professionals (AHPs) who are either new to or revisiting the realm of minor illness assessment and treatment. The course progression is designed to take you from foundational knowledge to more confident and adept management of patients, encompassing both adults and children.Course Details:Day One:- 09:15 AM: Coffee and registration- 09:30 AM: Introduction and course objectives- 09:40 AM: What constitutes a 'Good Consultation?'- 10:00 AM: Fever and Flu Like Illness- 10:45 AM: Coffee break- 11:00 AM: Respiratory Tract Infections (including breath sounds)- 13:00 PM: Lunch break- 14:00 PM: Case Studies- 14:30 PM: Urinary Tract Infections (UTIs)- 14:30 PM: Ears, Nose, and Throat conditions- 15:00 PM: Abdominal Pain- 15:30 PM: Action plan, evaluation, and resources- 15:45 PM: CloseDay Two:- 09:15 AM: Coffee and registration- 09:30 AM: Review of work from day 1 - any feedback/questions?- 09:45 AM: Head, Neck, and Back Pain- 10:30 AM: Eye Infections- 10:45 AM: Coffee break- 11:00 AM: Mental Health (low mood)- 13:00 PM: Lunch break- 13:45 PM: Rashes- 14:30 PM: Minor Injuries- 16:00 PM: Case Studies, Action plan, evaluation, and resources - next steps?- 16:15 PM: CloseLearning Outcomes:- How to conduct an effective consultation.- Enhanced understanding of diagnosing and treating specific minor illnesses.- Expanded knowledge of medicine management, including when and what to prescribe.- Understanding when to initiate tests for better illness management.- Ability to discuss the patient's options and proposed management plan effectively.- Knowing when to refer a patient to another health professional.- Encouraging discussions about relevant practice problems and their solutions.- Appreciating the importance of ongoing professional development.

Excel in Hybrid Team Management with our course tailored for today's workplace. Gain essential skills to lead hybrid teams effectively, from initiation to evaluation, ensuring high performance.

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Master ISO 9001:2015 Quality Management standards. From leadership to performance evaluation, this course provides a comprehensive guide to implementing effective quality management systems in your organization.

Supper Planning This course takes you through the wide parts of feast arranging. To effectively uphold customers' objectives, an all encompassing perspective on nourishment is required. The initial two modules give you an outline of the dietary parts that make up good dieting designs. With information on what the body needs to work ideally, the course proceeds to handle the fundamental nourishing evaluation instruments that you can use in your determination of spaces of dietary improvement. These wholesome evaluation instruments go connected at the hip with healthful systems that support an adjustment of eating practices and food decisions. The dietary systems canvassed shift in their appropriateness for customers of various profiles, so they can be utilized relying upon the degree of customer status and their obligation to change. At long last, the course investigates how to decide the validity of a source. This gives you the certainty to prompt customers properly and give sound healthful exhortation. What You Will Learn: Dietary standards and the parts of a smart dieting design Which job macronutrients play and their principle types, including explicit food varieties and their primary macronutrient parts Instructions to join the utilization of dietary evaluation devices, to distinguish spaces of progress inside a customer's eating routine The fundamental nourishing procedures, and how to apply your insight into dietary standards Instructions to assess wellsprings of dietary data The Benefits of Taking This Course: You will learn fundamental nourishing realities You can precisely recommend wholesome spaces of progress You can apply healthful information, to help customers' nourishing objectives You can give customers sound dietary guidance

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

Dive into the intricate world of event execution and evaluation. Cultivate operational excellence, navigate unexpected challenges, ensure safety and security, and harness the power of post-event analysis to continuously improve your event management skills. After the successful completion of the course, you will be able to learn about the following, Managing event operations, including event setup, staffing, and execution. Addressing unexpected challenges and changes during the event. Managing event safety and security. Importance of Event Evaluation. Conducting post-event evaluations. Developing recommendations for future events based on evaluation findings. Discover the intricacies of event marketing through this course, where you'll explore topics such as understanding diverse target audiences, creating powerful event branding, crafting effective marketing materials, utilizing appropriate channels, and analyzing promotional efforts. Gain insights into current marketing trends shaping the industry.   This course equips participants with hands-on expertise in executing flawless events. From managing operations and ensuring safety to addressing unforeseen challenges, participants will learn the art of post-event evaluations. The course culminates in the development of strategic recommendations, preparing graduates for diverse roles in the dynamic field of event execution and evaluation. Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Executing and Evaluating Events Self-paced pre-recorded learning content on this topic. Executing and Evaluating Events Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience. Event Operations Manager Event Safety Coordinator Event Execution Specialist Evaluation Analyst Event Manager Operations Coordinator Safety and Security Manager Event Planning Consultant Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.