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This course bundle is ideal for anyone looking to establish their Cisco networking career. It consists of one Cisco Certified Network Associate (CCNA) certification, one Cisco Certified DevNet Associate (CCDA) certification, and four Cisco Certified Networking Professional (CCNP) certifications. Although there are no formal prerequisites to enrol on the CCNA, CCDA, or CCNP certification courses and sit the exams, learners should have a good foundation knowledge in networking. The newly retooled CCNA covers a breadth of topics, including: Network fundamentals Network access IP connectivity IP services Security fundamentals Automation and programmability Achieving CCNA certification is the first step in preparing for a career in networking technologies. To earn your CCNA certification, you only need to pass one exam – which covers a broad range of fundamentals for IT careers, based on the latest networking technologies. The Cisco Certified DevNet Associate certification validates your skills and knowledge in understanding and using APIs, Cisco platforms and development, application development and security, and infrastructure and automation. Ideally, DevNet Associates candidates also have one or more years of experience with software development including Python programming. The CCNP is the next level from the CCNA and CCDA. As with the CCNA, there are no formal prerequisites to enrol on the CCNP certification course and sit for the exams. Learners taking a CCNP course generally have an in-depth knowledge of networking, as well as a good understanding of Cisco technologies. The typical certification path for most learners would begin with either the CCDA or CCNA, then progress onto the CCNP. Learners need to pass two exams (one core exam and one concentration exam) in order to gain one CCNP certification. This course bundle includes the core exam and three concentration exams. Once a learner has passed the core exam, they can choose to specialise in one or all three of the CCNP concentration areas listed in this course. The core exam’s focus is based around implementing and operating Cisco enterprise network core technologies.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Women in Local Government Leadership Workshop is for Local Government professionals looking to learn new skills with like-minded peers and further or advance their within Local government.

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Duration 2 Days 12 CPD hours This course is intended for The primary audiences for this course are quality directors and quality assurance managers, managers responsible for the governance of an enterprise and management of its risks, technical experts, project managers and consultants, internal auditors, compliance officers and virtually anybody involved in ANSI/TIA-942 compliance certification related projects either from an end-user or vendor perspective. Participants must hold a valid CTDC certificate in order to be able to register for the CTIA class. Overview After completion of the course the participant will be able to: 1. Prepare the organisation for an audit according to ANSI/TIA-942 including preparation of the required documents, resource planning and management of the audit process itself 2. Conduct an internal audit according to ANSI/TIA-942 following ISO-19011 guidelines 3. Facilitate and support the external audit to ANSI/TIA-942 4. Manage the post-audit process with respect to CAR (Corrective Action Reports), gap closing and _nal certification 5. Facilitate surveillance and recertification audits This intensive course builds further on the technical understanding of the standard acquired in the CTDC© (Certified TIA-942 Design Consultant) course. Fundamental concepts and process of an internal audit Terms and de_nitions Difference between an internal and external auditor Audit principles Auditor competency requirements Managing an audit programme Establishing the audit objectives Establishing the audit programme Planning the audit Planning the schedule Resource planning Tools/equipment required Document requirements - Design documents - Process documents - Declarations Conducting the audit Conducting an opening meeting Conducting the audit Interviews Document review Facility review Typical non-conformities - Architectura - Electrica - Mechanical - Telecommunications Preparing the audit conclusions Conducting the closing meeting Preparing and distributing the audit report Audit report requirements Classification of non-conformities Typical format of an audit report Following up on the audit The CAR ? Corrective Action Report Evaluation of the Corrective Action Report Requesting a formal external auditIssuing a conformity certificate Requirements of the certificate Registration of the certificate Surveillance auditsRecertification auditsExam: Certified TIA-942 Internal Auditor) Actual course outline may vary depending on offering center. Contact your sales representative for more information. Additional course details: Nexus Humans Certified TIA-942 Internal Auditor (CTIA) 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 Certified TIA-942 Internal Auditor (CTIA) 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.

This Diploma in Health and Social Care - Level 3 qualification (Accredited by Qualifi, UK) has been created to develop and reward the health and social care workers of today and the future, and to continue to bring recognition and professionalism to the health and social care sector. The rationale of this Level 3 Diploma in Health and Social Care programme is to provide a career path for learners who wish to develop their care capabilities within the health and social care sector. The programme provides the opportunity for individuals to forge a career in health and social care by seeking a greater knowledge and understanding industry, and to support the individual's development into senior positions. Program Overview: Diploma in Health and Social care - Level 3 Key Highlights of Level 3 Diploma in Health and Social care qualification are: Program Duration: 6 Months (Fast-Track Mode Available) Program Credits: 60 Credits Designed for working Professionals Format: Online No Written Exam. The Assessment is done via Submission of Assignment Tutor Assist available Dedicated Student Success Manager Timely Doubt Resolution Regular Networking Events with Industry Professionals Become eligible to gain direct entry into relevant Undergraduate degree programme. Alumni Status No Cost EMI Option (Instalment option is available on LSBR Website) Aims of this Level 3 Diploma in Health and Social Care qualification To equip individuals with the knowledge, understanding and skills required for success in employment in the health and social care sector To enable progression to the first year of a degree or related professional qualification To provide specialist study relevant to individual vocations and environments in which learners are currently working, or to which learners are aiming to work, within the health and/or social care sector To develop learners' ability to contribute positively to good practice in the health and social care environment through effective use and combination of the knowledge and skills gained in the qualifications To develop skills and techniques, personal qualities and attributes essential for successful performance in working life and thereby enabling learners to make an immediate contribution to employment. Mandatory Units: The Qualifi Level 3 Diploma in Health and Social Care qualification consists of 4 mandatory units for a combined total of 40 credits An introduction to Health and Social Care (10 credits) Communication for Health and Social Care (10 credits) Promoting Health in the Population (10 Credits) Person-Centred Care (10 credits) Optional Units: The learner must also achieve a minimum of 20 credits from these units. Understanding Diabetes Care (10 credits) Understanding Stroke Care (10 credits) Understanding Dementia Care (10 credits) Who is this course for? Working Professionals, O-Level holders and those who wish to progress in their Career. Requirements The Level 3 Diploma in Health and Social Care (Accredited by Qualifi) qualifications has been designed to be accessible without artificial barriers that restrict access and progression. Learners are expected to hold the following: Qualifications at Level 2 and/or Work experience in the health and social care sector and demonstrate ambition with clear career goals, or A Level 3 qualification in another discipline and want to develop their careers in health and social care. In certain circumstances, learners with considerable experience but no formal qualifications may be considered Career path Career Progression Learners completing the Level 3 Diploma in Health and Social Care can progress to: Level 4 Diploma in Health and Social Care, or The First Year of Undergraduate study in Health and Social Care, or Directly into employment in an associated profession.

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).