13842 AI courses delivered Online

What does this course cover? This is an online course for developing history leadership in primary teaching. This term, we will be piloting the course as an immersive programme covering the key elements of primary history subject leadership, enabling all involved in history leadership at primary to carry out their role effectively. How is this course structured and delivered? The course will take place in ten online sessions from January–May 2025. Sessions 1, 8 and 10 are compulsory to attend live. Participants will be required to complete a gap task after each session and contribute to an online reflection diary. The other sessions will be recorded for participants to work through at their convenience, though participants are encouraged to attend the recording sessions live in order to make the most of the programme. All recordings will be made available on the course Moodle page within two working days of the live recording session. Who is this course for? This programme is designed for anyone who is a subject coordinator or subject leader for history in their primary school. You may have just been appointed as a subject leader, or be more experienced but looking for formal training or fresh inspiration and direction. What are the outcome? This course will: equip you fully for the demands of history leadership in primary schools increase your confidence to develop an innovative, inclusive and effective history curriculum in your school develop your understanding of efficient action planning and how to make a wider impact help you to make the case for history with senior leaders enable you to enthuse others and lead staff meetings about history develop your understanding of current issues in primary history education The course will include a chance to share resources and assessment approaches, plus much more What will each session cover? Session 1: Introductory meeting (Live attendance) Wednesday 22 January 2025, 4pm–5.30pm Introduction and aims Reflection activity on the current state of history in your school Key responsibilities of the history subject leader Current Ofsted implications for the subject and what to expect in an inspection How the HA and latest Ofsted material can support you Session 2: What makes an effective history subject leader? (Live and recorded) Monday 27 January 2025, 4pm–5.30pm How to effectively support teachers and learners as a history leader How to create a vision, lead change, and manage time Effective action planning and improvement Auditing your history provision and building your evidence trail Developing an innovative, inclusive and effective history curriculum in your school Disciplinary knowledge: What is history? What do historians do? Suggested gap task: Conducting teacher and/or pupil voice questionnaires. Compulsory gap task: Add to your reflection diary for the programme. If relevant, reflect on how you will use the teacher questionnaire or pupil voice. Session 3: What makes effective teaching of history? (Live and recorded) Wednesday 19 February 2025, 4–5.30pm The role of the teacher Supporting SEND pupils Securing substantive knowledge and concepts across the curriculum How to support colleagues by identifying core knowledge How to involve the whole staff in mapping out and embedding the progression of concepts across the curriculum Suggested gap task: Consider SEND in your curriculum. Compulsory gap task: Add to your reflection diary for the programme reflecting on SEND. Session 4: Curriculum and practice at EYFS (Live and recorded) Tuesday 25 February 2025, 4–5.30pm Unpicking and supporting the requirements of the EYFS curriculum and Development Matters Developing historical and chronological understanding in EYFS as part of Understanding the world Progression and transition from EYFS to Key Stage 1 Ensuring coherence: how to start developing concepts Making use of the local history on your doorstep for EYFS and KS1 Accessing resources and support for EYFS Suggested gap task: Fact finding and liaison. Compulsory gap task: Add to your reflection diary for the programme reflecting on current EYFS practice. Session 5: Curriculum and practice at Key Stage 1 (Live and recorded) Monday 3 March 2025, 4pm–5.30pm Unpicking and supporting the requirements of the National Curriculum for KS1 Progression: building upon transition from EYFS What do KS1 require before KS2? Ensuring coherence: further developing the school’s chosen substantive concepts How to develop disciplinary concepts Developing appropriate chronological understanding in KS1 Enhancing and engaging children’s knowledge and retention through enquiry The use of working walls in KS1 to support learning Accessing resources and support for KS1 Suggested gap task: Check your KS1 curriculum for coverage and progression, building upon what they have learnt in EYFS. Compulsory gap task: Add to your reflection diary for the programme reflecting on KS1 practice in your setting. Session 6: Curriculum and practice at Key Stage 2 (Live and recorded) Tuesday 11 March 2025, 4pm–5.30pm Unpicking and supporting the requirements of the National Curriculum for KS2 How to incorporate local history with progression in KS2 What do we want the children in Year 6 to leave with? Developing appropriate chronological understanding in KS2 Preparing for transition to Key Stage 3 Ensuring coherence: building upon the school’s chosen concepts for progression What the HA can do to support the teaching of KS2 units? Resources and support for KS2 Suggested gap task: Check your KS2 curriculum for coverage and progression, building upon what they have learnt in EYFS and KS1. Compulsory gap task: Add to your reflection diary for the programme reflecting on KS2 practice in your setting. Session 7: Approaching sensitive issues in history (Live and recorded) Tuesday 18 March 2025, 4pm–5.30pm What do we mean by diversity in history and why is it important? How to ensure a wider diversity in your history teaching Barriers to making the curriculum more diverse and how to overcome them Approaches to sensitive issues in history lessons, including: - Refugees and migration history - War - Climate change Suggested gap task: Consider opportunities for increasing diverse voices and raising climate issues in your curriculum. Compulsory gap task: Add to your reflection diary for the programme considering how you might address the sensitive or current issues raised in this session. Session 8: Interim review meeting (Live attendance) Tuesday 25 March 2025, 4pm–5.30pm A review session allowing subject leaders to reflect upon their progress so far Opportunity to respond to suggestions, clarify information, or raise issues from the previous sessions Time to ask questions, share good practice or recommended resources with the rest of the cohort Suggested gap task: Time to follow up on anything raised in this session or to complete any previous tasks, following clarification and inspiration from this session. Compulsory gap task: Add to your reflection diary for the programme, reflecting on your priorities and actions using suggestions from the presenter or other delegates. Session 9: Assessment and evidence (Live and recorded) Wednesday 30 April 2025, 4pm–5.30pm Assessment and expectations A word about writing Triangulating the evidence What to look for in a work scrutiny Suggested gap task: Conduct a work scrutiny on one aspect to check across the school, in conjunction with a focused pupil voice on the same aspect. Compulsory gap task: Add to your reflection diary considering how you will conduct a work scrutiny and what you will focus on – or, if relevant, reflect on what you discovered and how you will address any issues. Session 10: Final meeting (Live attendance) Wednesday 14 May 2025, 4pm–5.30pm Following up any requests arising from the interim meeting (Session 8) Discussion or clarification of any issues arising from previous sessions Discussion of priorities, next steps and any issues Sharing ideas for enthusing others and leading staff meetings: “What worked for me?” Communicating with headteachers, governors, colleagues and parents Are you ready for the HA Quality Mark? Suggested gap task: Identifying your school’s next steps Compulsory gap task: Complete your final piece in your reflection diary.

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Take a step toward a brighter future with our Healthcare Assistant course! *** Course Curriculum *** Here is the curriculum breakdown of the Healthcare Assistant at QLS Level 5 course: Working in Different Healthcare Settings Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 1 Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 2 Maintaining Medical Records Confidentiality in a Medical Environment Health and Safety Responsibilities Hygiene in Nursing Infection Control Mobility and Immobility Issues of Patients in Nursing Rights and Responsibilities as a Health and Social Care Worker Role as A Caregiver and Healthcare Professional Providing Care or Treatment to People Who Lack Capacity Managing Service Delivery in Health and Social Care Medical Jargon and Terminology Effects of Covid-19 on Human Life Preventions and Social Measures to Be Taken Information Technology in Health Care Artificial Intelligence, Data Science and Technological Solutions against Covid-19 Assessment Process You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. 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Unlock the secrets of successful recruitment with our 'Recruitment Consultant: Process and Strategies' course. Master the art of candidate assessment, build lasting client relationships, and stay ahead with cutting-edge recruitment strategies and technology. Navigate legal and ethical considerations, specialize in executive search, and explore global talent acquisition. Elevate your career in HR and staffing. Enroll now to become a proficient recruitment consultant and transform the way organizations connect with top-tier talent.

Whetstone Communications and comms2point0 are pleased to bring you the Data Bites series of free webinars. Our aim is to boost interest and levels of data literacy among not-for-profit communicators.

The two-day Adult MHFAider® courses are tailored around supporting adults, and can be attended by anyone from age 16 upwards. A lot of the education settings we work with have now trained a number of Adult MHFAider®’s to support their colleagues as part of their whole-school/college approach to mental health and wellbeing.

Overview This comprehensive course on Tic-Tac-Toe Clone - The Complete SFML C++ Game Course will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Tic-Tac-Toe Clone - The Complete SFML C++ Game Course comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Tic-Tac-Toe Clone - The Complete SFML C++ Game Course. It is available to all students, of all academic backgrounds. Requirements Our Tic-Tac-Toe Clone - The Complete SFML C++ Game Course is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 8 sections • 20 lectures • 03:27:00 total length •Introduction: 00:02:00 •Project Setup On Windows: 00:12:00 •Project Setup On Mac: 00:10:00 •State Machine: 00:08:00 •Asset Manager: 00:08:00 •Input Manager: 00:08:00 •Game Loop: 00:14:00 •Splash State/Screen: 00:19:00 •Main Menu State/Screen: 00:20:00 •Game Over State/Screen: 00:08:00 •Pause State/Screen: 00:14:00 •Game Over State/Screen: 00:08:00 •Grid Part 1 - Initialisation: 00:14:00 •Grid Part 2 - Placing A Piece: 00:12:00 •Check For Win Condition: 00:20:00 •AI: 00:21:00 •Show Game Over Screen: 00:05:00 •What to Do Next?: 00:04:00 •Resource: 00:00:00 •Assignment - Tic-Tac-Toe Clone - The Complete SFML C++ Game Course: 2 days

Unlock the keys to unparalleled customer service excellence with our 'Call Centre Training' course. Dive into the essentials of call center operations, hone communication skills, master telephone etiquettes, and learn effective negotiation techniques. Navigate challenging interactions, manage difficult customers, and grasp the intricacies of call center metrics and benchmarking. Stay ahead of the curve with insights into the latest technology trends shaping the industry. Elevate your career and contribute to organizational success by enrolling today. Develop the skills needed to excel as a call center professional and create lasting customer satisfaction.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Venture into the realm of digital advancements with 'Introduction to Digital and Emerging Technologies'. Transform your curiosity into insightful knowledge.