2626 Evaluation courses

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.

This International Practitioners of Holistic Medicine accredited qualification teaches the skills, knowledge and competence required of an individual in order to work with clients on specific breathing techniques in both 1:1 or group sessions. Learners will cover: The anatomy of the lungs; gaseous exchange; the breath-brain connection and the physical and mental benefits of breathwork. How to hold space for breathwork; creating a trauma-informed space and basic facilitation skills. How the breath impacts the vagus nerve and its connection to the parasympathetic nervous system. The structure of a breathwork session and how to build momentum with music and motivational cueing (i.e.what to say and what to play). The content of a breathwork session with an in-depth study of 6 unique breathwork exercises. Advanced Facilitation skills – How to plan for emotional and physical reactions; how to modify for different audiences/special populations.

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.

EnergyEdge's classroom training provides in-depth knowledge on project economics, risk assessment, and decision analysis for the oil and gas industry. Take your skills to the next level.

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

Course Information In today's evolving landscape, pharmacovigilance (PV) systems face ongoing challenges due to global, national, and company-specific events. This course focuses on developing personnel equipped to navigate these complexities and improve the safe use of medicinal products. We emphasise continuous global thinking, communication, and strategic planning, ensuring adaptability across various levels. The course explores maintaining PV system functionality while adhering to regulatory requirements. Participants will apply European regulatory standards to enhance and sustain PV system effectiveness, contributing to improvement initiatives and ensuring operational integrity. The course covers PV system intricacies, regulatory compliance, and a 'systems approach' for auditors, quality assurance personnel, and PV practitioners. Through presentations and workshops, attendees gain insights into implementing and maintaining an effective PV system. Who should attend? Auditors Pharmacovigilance Quality System Managers Pharmacovigilance scientists The QPPV. Course benefits Throughout the course delegates will explore application of the legal requirements for the PV system and quality system and how to assure these systems. They will demonstrate their ability to contribute to: A systematic investigation of the pharmacovigilance system and its quality system Examination of how the pharmacovigilance system and quality system interact to achieve compliance. The risk-based approach to auditing the PV system and quality system The maintenance of 'inspection readiness' Explore how to investigate the complex PV system Discussions about how to monitor and maintain the PV system and assure compliance. Course Objectives Clarify what has to be done: Explore application of the legal requirements. Explore how to do what has to be done: Adopt a systemic approach to systematically investigate or implement and maintain the PV system and quality system Examine how a compliant PV system and a compliant quality system interact to achieve compliance with regulatory requirements for PV Explore how to investigate the complexity of the PV system. Discus how to identify what is missing or what needs to be improved: Discuss how to monitor and maintain the PV system and assure compliance. This course will assist delegates with: An understanding of key system principles, A practical approach to implementing, maintaining and monitoring the PV system and its quality system A procedure to share expertise to increase efficiency and confidence. This course is structured to encourage delegates to: Discuss and develop ideas - Share knowledge and experiences - Solve specific problems.  By the end of the course delegates will be able to: Understand better the pharmacovigilance system, its quality system and how the components interact to achieve the objectives of pharmacovigilance Investigate, and analyse the pharmacovigilance system and to identify what is missing and what needs to be improved. Tutors Tutors will be comprised of (click the photos for biographies): Jana Hyankova Head of PV Department, IVIGEE Services a.s. Programme Please note timings may be subject to alteration. Day 1 08:30 Welcome, registration, course objectives and introduction to work groups Housekeeping notices, meet other delegates, explore how to work in your work group, course objectives. Clarify the definition and objectives of Pharmacovigilance. 09:30 The Regulatory Framework for Pharmacovigilance Identify the relevant regulations and directives. Explore GVP guidance, structure of the modules and standard format of each module. 10:00 The Pharmacovigilance System Exploration of how to organise what has to be done, communications. Exploration of the structures and processes for pharmacovigilance. 10:30 Break 11:00 Workshop 1 and Feedback Exploring an organisational model of the pharmaceutical company- cooperation between PV and other stakeholders. 12:00 The Quality System for pharmacovigilance Exploration of the structures, processes for the PV quality system and discussion of how it interacts with the pharmacovigilance system to meet the objectives of pharmacovigilance effectively and efficiently. 13:00 Lunch 14:00 Workshop 2 and Feedback The quality system puzzle. Explore the organisation of the PV quality system and how it interacts with the PV system. 14:30 The Quality System for pharmacovigilance Exploration of the structures processes for the PV quality system and discussion of how it interacts with the pharmacovigilance system to meet the objectives of pharmacovigilance effectively and efficiently. 15:00 Description of PV System 15:30 Break 15:30 Workshop 3 and Feedback The quality system puzzle Explore the organisation of the PV quality system and how it interacts with the PV system. 16:00 The Pharmacovigilance Safety Master File Construction of the Pharmacovigilance System Master File and its purpose. 17:00 Workshop 3 and Feedback Description of PV System. 18:00 End of Day Day 2 08:30 Drug Safety in the Clinical Trial Environment - Part 1 Information flow and responsibilities of the sponsor. 09:30 Workshop 4 and Feedback Drug Safety in the Clinical Trial environment: Information flow and responsibilities of the sponsor. 10:30 Break 11:00 Drug Safety in the Clinical Trial Environment - Part 2 Information flow and responsibilities of the sponsor. 12:00 Lunch 13:00 Workshop 5 and Feedback Drug Safety in the clinical trial environment: Information flow and responsibilities of the sponsor. 13:30 Processing of Safety Data Exploration of safety data processing, verification, validation, follow up, formatting and collation, reporting requirements, quality and data management. 15:00 Break 15:30 EudraVigilance Exploration of how EudraVigilance supports the PV system. 16:15 Signal Detection and Evaluation/Risk Benefit Assessment: Pharmacovigilance Risk Assessment Committee (PRAC): What is a signal? What are the regulatory requirements? How is signal detection and evaluation conducted? Qualitative and quantitative methods of signal detection. Risk benefit assessment. 17:00 Risk Management Plans A cornerstone of Pharmacovigilance safety communications, direct healthcare professional communication 18:00 End of Day Day 3 08:30 The Pharmacovigilance Risk Assessment Committee (PRAC) Exploration of how good practice is achieved. Composition, role and responsibilities. Examples of referrals. 09:15 Development Safety Update Reports (DSURs): Regulatory requirements, exploring good practice, report format, reference safety information, schedule of submission, analysis evaluations and distribution. 10:00 Periodic Safety Update Reports (PSURs)/Periodic Benefit Risk Evaluation Reports (PBRERs) Regulatory requirements, exploring good practice, report format, reference safety information, schedule of submission, analysis evaluations and distribution. 10:30 Break 11:00 Periodic Safety Update Reports (PSURs)/Periodic Benefit Risk Evaluation Reports (PBRERs) Regulatory requirements, exploring good practice, report format, reference safety information, schedule of submission, analysis evaluations and distribution. 12:00 Workshop 6 and Feedback To explore the compilation and submission of the PSUR. 13:00 Lunch 13:30 Role of the QPPV Exploration of the legal responsibilities of the QPPV and the MAH. 14:30 Break 15:00 Workshop 7 and Feedback To explore the challenges faced by the QPPV. 15:30 End of course Extra Information Face-to-Face Course Course material This course will be run completely online. You will receive an email with a link to our online system, which will house your licensed course materials and access to the remote event. Please note this course will run in UK timezone. The advantages of this include: Ability for delegates to keep material on a mobile device< Ability to review material at any time pre and post course Environmental benefits – less paper being used per course Access to an online course group to enhance networking You will need a stable internet connection, a microphone and a webcam. CPD Points 23 Points   Development Level Develop

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.

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

Who is this course suitable for? Required to undertake asbestos fibre counting as part of their work Considering a career in asbestos analysis Responsible for managing asbestos analysts Prior Knowledge and Understanding Candidates for this course are expected to be aware of HSG 248 Asbestos: The Analysts' Guide (July 2021), and in particular Appendix 1, Fibres in air: sampling and evaluation of by phase contrast microscopy. Candidates will preferably have prior experience of analysing fibre count samples and may already be participating in a quality control scheme. In addition, candidates are expected to have had training to cover the core competencies outlined within the foundation material detailed within Table A9.1 of HSG248 Asbestos: The Analysts' Guide (July 2021). This may be achieved by In -house learning or through the P400 foundation module.