7 Sciences courses about Learning in Leicester delivered Live Online

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

Welcome to GLA Tutors, a pioneering platform for primary school tutoring and SATs preparation.  At GLA Tutors, we understand the importance of a strong foundation in primary school subjects and strive to provide comprehensive support to help children excel in their academic journey. Our team of expert tutors is well-versed in the English National Curriculum, which forms the basis of primary school education in the UK. We have carefully analysed the curriculum requirements to ensure that our tutoring services cover all key subjects and align with the learning objectives set by the curriculum. Our provision for tutoring all primary school subjects encompasses a wide range of subjects, including: 1. English: - Reading comprehension - Vocabulary development - Grammar and punctuation - Writing skills 2. Mathematics: - Number and place value - Addition, subtraction, multiplication, and division - Fractions, decimals, and percentages - Geometry and measurement 3. Science: - Investigating scientific phenomena - Understanding the natural world - Conducting experiments and making observations - Developing scientific thinking and inquiry skills 4. History: - Understanding historical events and timelines - Exploring significant figures and civilizations - Analysing primary and secondary sources - Developing historical knowledge and critical thinking skills 5. Geography: - Studying different landscapes and environments - Investigating human and physical geography - Exploring global issues and sustainability - Developing geographical skills and understanding At GLA Tutors, we understand that preparing children for SATs can be a challenging task. Our tutors are well-versed in the SATs format and content, and they provide targeted support to help children excel in their exams. We cover all areas of the SATs, including English (reading, grammar, and writing) and Mathematics, ensuring that children are well-prepared and confident on exam day. Our tutoring sessions are designed to be engaging and interactive, fostering a love for learning and encouraging children to reach their full potential. We use a variety of teaching techniques, including hands-on activities, visual aids, and interactive resources, to make learning enjoyable and effective. With GLA Tutors, you can be assured that your child will receive top-quality tutoring in all primary school subjects and be well-prepared for SATs. Our tutors are committed to providing personalised support, tailoring their teaching methods to meet the unique learning needs of each child. Join us and let us help your child thrive academically and achieve success in their primary school journey and SATs.

Unlocking Success in GCSE Geography! At GLA Tutors, we are dedicated to helping students excel in their GCSE Geography examinations. Our experienced tutors are passionate about the subject and committed to providing comprehensive support tailored to the AQA examination board's specification. Let's dive into the breakdown of the AQA GCSE Geography specification: Paper 1: Living with the Physical Environment This paper focuses on understanding natural landscapes, such as rivers, coasts, and ecosystems. Our tutors will guide students through topics like the water cycle, coastal processes, and the impact of climate change. We offer in-depth explanations, interactive activities, and exam-style practice to build a solid foundation. Paper 2: Challenges in the Human Environment This paper explores the relationship between humans and their environment, including urban areas, development, and global issues. Our tutors will delve into concepts like population dynamics, urbanisation, sustainable development, and global inequalities. Through engaging discussions and real-world examples, we help students grasp the complexities of human geography. Paper 3: Geographical Applications In this paper, students apply their geographical skills to investigate real-world issues and carry out fieldwork. Our tutors will guide students through the process of designing and conducting fieldwork, collecting and analysing data, and presenting their findings. We provide practical guidance, research resources, and feedback to develop strong investigative skills. At GLA Tutors we go beyond the specification to nurture a deep understanding of geography. Our tutors create a supportive and engaging learning environment that encourages critical thinking, analysis, and effective exam techniques. We offer personalised one-on-one sessions, group discussions, and access to a range of learning materials to cater to each student's needs. Whether it's understanding the intricacies of physical processes or analysing the complexities of human interactions, our tutors are here to guide students towards success in their GCSE Geography journey. Join us and unlock your full potential in GCSE Geography! Feel free to explore our website for more information or reach out to us with any questions you may have. Let's embark on this exciting learning adventure together!  https://www.globallearners.academy/services/gcse We can provide assistance for everything you need to prepare students for exams, including: past papers, mark schemes and examiners’ reports specimen papers and mark schemes for new courses exemplar student answers with examiner commentaries high quality revision guides

Learn everything you need to know to be fully competent with Window OS. This syllabus takes you around the basics and then on another deep dive into all the elements. Discover things you never knew and speed up your experience using Windows OS. Module 1: Introduction to Windows OS • Understanding the Windows operating system • Navigating the Windows interface • Customizing system settings and preferences Module 2: File Management • Managing files and folders in Windows Explorer • Copying, moving, and renaming files • Using the Recycle Bin and data recovery Module 3: Windows Built-in Apps • Using Microsoft Edge for web browsing • Effective web searching using search engines • Email management with Windows Mail • Calendar and task management with Windows Calendar Module 4: Software Installation and Updates • Installing and updating software applications • Managing and uninstalling programs • Windows Store and app installations Module 5: Microsoft Office Basics • Introduction to Microsoft Office suite • Using Microsoft Word for document creation • Basic spreadsheet management with Microsoft Excel Module 6: Microsoft Office Intermediate Skills • Advanced features in Microsoft Word • Creating and formatting spreadsheets in Microsoft Excel • Creating dynamic presentations with PowerPoint Module 7: Multimedia and Graphics • Basic image editing with Paint • Using Windows Photo app for photo management • Creating graphics with Paint 3D Module 8: Productivity and Collaboration • Using OneDrive for cloud-based storage and collaboration • Working with Windows Sticky Notes and To-Do • Collaborative editing with Microsoft Office Online Module 9: Troubleshooting and Maintenance • Identifying and resolving common Windows issues • Using Task Manager for performance monitoring • Maintenance tasks for Windows OS Module 10: Windows Security and Privacy • Overview of Windows security features • Online safety and privacy best practices • Protecting personal data and devices Module 11: Advanced Windows Features • Customizing the Windows Start Menu and Taskbar • Using Cortana for voice commands and search • Virtual desktops and advanced multitasking Module 12: Using AI and Chat GPT • Introduction to AI and Chat GPT technology • Exploring AI-powered features in Windows • Using Chat GPT for productivity and assistance Module 13: Browsing and Search Engines • Effective use of web browsers • Utilizing search engines for research • Online safety and privacy while browsing Module 14: Cybersecurity • Understanding cybersecurity threats • Protecting against malware and phishing attacks • Secure online practices and password management Module 15: Software Installation and Factory Reset • Installing and updating software applications • Factory resetting a Windows device • Data backup and recovery during resets Module 16: Final Projects and Assessment • Culminating projects showcasing Windows OS skills • Practical exams assessing Windows software knowledge and skills • Preparing for industry-recognized certifications (optional) Please note that the duration and depth of each module can vary depending on the level of expertise required and the specific needs of the learners. Additionally, it's important to adapt the curriculum to the learners' proficiency levels, whether they are A Level/GCSE students or adult learners with different experience levels.

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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

Duration 5 Days 30 CPD hours This course is intended for This course is designed for application developers. Overview Describe the benefits of implementing a decision management solution with Operational Decision Manager.Identify the key user roles that are involved in designing and developing a decision management solution, and the tasks that are associated with each role.Describe the development process of building a business rule application and the collaboration between business and development teams.Set up and customize the Business Object Model (BOM) and vocabulary for rule authoring. Implement the Execution Object Model (XOM) that enables rule execution.Orchestrate rule execution through ruleflows. Author rule artifacts to implement business policies.Debug business rule applications to ensure that the implemented business logic is error-free.Set up and customize testing and simulation for business users.Package and deploy decision services to test and production environments.Integrate decision services for managed execution within an enterprise environment.Monitor and audit execution of decision services.Work with Operational Decision Manager features that support decision governance. This course introduces developers to IBM Operational Decision Manager V8.9.2. It teaches participants the concepts and skills required to design, develop, and integrate a business rule solution with Operational Decision Manager. This course begins with an overview of Operational Decision Manager, which is composed of two main environments: Decision Server for technical users and Decision Center for business users. The course outlines the collaboration between development and business teams during project development. Through instructor-led presentations and hands-on lab exercises, participants learn about the core features of Decision Server, which is the primary working environment for developers. Participants design decision services and work with the object models that are required to author and execute rule artifacts. Participants gain experience with deployment and execution, and work extensively with Rule Execution Server. In addition, students become familiar with rule authoring so that you can support business users to set up and customize the rule authoring and validation environments. Participants also learn how to use Operational Decision Manager features to support decision governance. Introducing IBM Operational Decision Manager Exercise: Operational Decision Manager in action Developing decision services Exercise: Setting up decision services Programming with business rules and developing object models Exercise: Working with the BOM Exercise: Refactoring Orchestrating ruleset execution Exercise: Working with ruleflows Authoring rules Exercise: Exploring action rules Exercise: Authoring action rules Exercise: Authoring decision tables Customizing rule vocabulary with categories and domains Exercise: Working with static domains Exercise: Working with dynamic domains Working with queries Exercise: Working with queries Debugging rules Exercise: Executing rules locally Exercise: Debugging a ruleset Enabling tests and simulations Exercise: Enabling rule validation Managing deployment Exercise: Managing deployment Exercise: Using Build Command to build RuleApps Executing rules with Rule Execution Server Exercise: Exploring the Rule Execution Server console Auditing and monitoring ruleset execution Exercise: Auditing ruleset execution through Decision Warehouse Working with the REST API Exercise: Executing rules as a hosted transparent decision service (HTDS) Additional course details: Nexus Humans WB402 IBM Developing Rule Solutions in IBM Operational Decision Manager V8.9.2 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 WB402 IBM Developing Rule Solutions in IBM Operational Decision Manager V8.9.2 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.