124 Courses in Coventry delivered Live Online

Duration 4 Days 24 CPD hours This course is intended for The Microsoft Security Operations Analyst collaborates with organizational stakeholders to secure information technology systems for the organization. Their goal is to reduce organizational risk by rapidly remediating active attacks in the environment, advising on improvements to threat protection practices, and referring violations of organizational policies to appropriate stakeholders. Responsibilities include threat management, monitoring, and response by using a variety of security solutions across their environment. The role primarily investigates, responds to, and hunts for threats using Microsoft Sentinel, Microsoft Defender for Cloud, Microsoft 365 Defender, and third-party security products. Since the Security Operations Analyst consumes the operational output of these tools, they are also a critical stakeholder in the configuration and deployment of these technologies. Learn how to investigate, respond to, and hunt for threats using Microsoft Sentinel, Microsoft Defender for Cloud, and Microsoft 365 Defender. In this course you will learn how to mitigate cyberthreats using these technologies. Specifically, you will configure and use Microsoft Sentinel as well as utilize Kusto Query Language (KQL) to perform detection, analysis, and reporting. The course was designed for people who work in a Security Operations job role and helps learners prepare for the exam SC-200: Microsoft Security Operations Analyst. Prerequisites Basic understanding of Microsoft 365 Fundamental understanding of Microsoft security, compliance, and identity products Intermediate understanding of Windows 10 Familiarity with Azure services, specifically Azure SQL Database and Azure Storage Familiarity with Azure virtual machines and virtual networking Basic understanding of scripting concepts. 1 - Introduction to Microsoft 365 threat protection Explore Extended Detection & Response (XDR) response use cases Understand Microsoft Defender XDR in a Security Operations Center (SOC) Explore Microsoft Security Graph Investigate security incidents in Microsoft Defender XDR 2 - Mitigate incidents using Microsoft 365 Defender Use the Microsoft Defender portal Manage incidents Investigate incidents Manage and investigate alerts Manage automated investigations Use the action center Explore advanced hunting Investigate Microsoft Entra sign-in logs Understand Microsoft Secure Score Analyze threat analytics Analyze reports Configure the Microsoft Defender portal 3 - Protect your identities with Microsoft Entra ID Protection Microsoft Entra ID Protection overview Detect risks with Microsoft Entra ID Protection policies Investigate and remediate risks detected by Microsoft Entra ID Protection 4 - Remediate risks with Microsoft Defender for Office 365 Automate, investigate, and remediate Configure, protect, and detect Simulate attacks 5 - Safeguard your environment with Microsoft Defender for Identity Configure Microsoft Defender for Identity sensors Review compromised accounts or data Integrate with other Microsoft tools 6 - Secure your cloud apps and services with Microsoft Defender for Cloud Apps Understand the Defender for Cloud Apps Framework Explore your cloud apps with Cloud Discovery Protect your data and apps with Conditional Access App Control Walk through discovery and access control with Microsoft Defender for Cloud Apps Classify and protect sensitive information Detect Threats 7 - Respond to data loss prevention alerts using Microsoft 365 Describe data loss prevention alerts Investigate data loss prevention alerts in Microsoft Purview Investigate data loss prevention alerts in Microsoft Defender for Cloud Apps 8 - Manage insider risk in Microsoft Purview Insider risk management overview Create and manage insider risk policies Investigate insider risk alerts Take action on insider risk alerts through cases Manage insider risk management forensic evidence Create insider risk management notice templates 9 - Investigate threats by using audit features in Microsoft Defender XDR and Microsoft Purview Standard Explore Microsoft Purview Audit solutions Implement Microsoft Purview Audit (Standard) Start recording activity in the Unified Audit Log Search the Unified Audit Log (UAL) Export, configure, and view audit log records Use audit log searching to investigate common support issues 10 - Investigate threats using audit in Microsoft Defender XDR and Microsoft Purview (Premium) Explore Microsoft Purview Audit (Premium) Implement Microsoft Purview Audit (Premium) Manage audit log retention policies Investigate compromised email accounts using Purview Audit (Premium) 11 - Investigate threats with Content search in Microsoft Purview Explore Microsoft Purview eDiscovery solutions Create a content search View the search results and statistics Export the search results and search report Configure search permissions filtering Search for and delete email messages 12 - Protect against threats with Microsoft Defender for Endpoint Practice security administration Hunt threats within your network 13 - Deploy the Microsoft Defender for Endpoint environment Create your environment Understand operating systems compatibility and features Onboard devices Manage access Create and manage roles for role-based access control Configure device groups Configure environment advanced features 14 - Implement Windows security enhancements with Microsoft Defender for Endpoint Understand attack surface reduction Enable attack surface reduction rules 15 - Perform device investigations in Microsoft Defender for Endpoint Use the device inventory list Investigate the device Use behavioral blocking Detect devices with device discovery 16 - Perform actions on a device using Microsoft Defender for Endpoint Explain device actions Run Microsoft Defender antivirus scan on devices Collect investigation package from devices Initiate live response session 17 - Perform evidence and entities investigations using Microsoft Defender for Endpoint Investigate a file Investigate a user account Investigate an IP address Investigate a domain 18 - Configure and manage automation using Microsoft Defender for Endpoint Configure advanced features Manage automation upload and folder settings Configure automated investigation and remediation capabilities Block at risk devices 19 - Configure for alerts and detections in Microsoft Defender for Endpoint Configure advanced features Configure alert notifications Manage alert suppression Manage indicators 20 - Utilize Vulnerability Management in Microsoft Defender for Endpoint Understand vulnerability management Explore vulnerabilities on your devices Manage remediation 21 - Plan for cloud workload protections using Microsoft Defender for Cloud Explain Microsoft Defender for Cloud Describe Microsoft Defender for Cloud workload protections Enable Microsoft Defender for Cloud 22 - Connect Azure assets to Microsoft Defender for Cloud Explore and manage your resources with asset inventory Configure auto provisioning Manual log analytics agent provisioning 23 - Connect non-Azure resources to Microsoft Defender for Cloud Protect non-Azure resources Connect non-Azure machines Connect your AWS accounts Connect your GCP accounts 24 - Manage your cloud security posture management? Explore Secure Score Explore Recommendations Measure and enforce regulatory compliance Understand Workbooks 25 - Explain cloud workload protections in Microsoft Defender for Cloud Understand Microsoft Defender for servers Understand Microsoft Defender for App Service Understand Microsoft Defender for Storage Understand Microsoft Defender for SQL Understand Microsoft Defender for open-source databases Understand Microsoft Defender for Key Vault Understand Microsoft Defender for Resource Manager Understand Microsoft Defender for DNS Understand Microsoft Defender for Containers Understand Microsoft Defender additional protections 26 - Remediate security alerts using Microsoft Defender for Cloud Understand security alerts Remediate alerts and automate responses Suppress alerts from Defender for Cloud Generate threat intelligence reports Respond to alerts from Azure resources 27 - Construct KQL statements for Microsoft Sentinel Understand the Kusto Query Language statement structure Use the search operator Use the where operator Use the let statement Use the extend operator Use the order by operator Use the project operators 28 - Analyze query results using KQL Use the summarize operator Use the summarize operator to filter results Use the summarize operator to prepare data Use the render operator to create visualizations 29 - Build multi-table statements using KQL Use the union operator Use the join operator 30 - Work with data in Microsoft Sentinel using Kusto Query Language Extract data from unstructured string fields Extract data from structured string data Integrate external data Create parsers with functions 31 - Introduction to Microsoft Sentinel What is Microsoft Sentinel? How Microsoft Sentinel works When to use Microsoft Sentinel 32 - Create and manage Microsoft Sentinel workspaces Plan for the Microsoft Sentinel workspace Create a Microsoft Sentinel workspace Manage workspaces across tenants using Azure Lighthouse Understand Microsoft Sentinel permissions and roles Manage Microsoft Sentinel settings Configure logs 33 - Query logs in Microsoft Sentinel Query logs in the logs page Understand Microsoft Sentinel tables Understand common tables Understand Microsoft Defender XDR tables 34 - Use watchlists in Microsoft Sentinel Plan for watchlists Create a watchlist Manage watchlists 35 - Utilize threat intelligence in Microsoft Sentinel Define threat intelligence Manage your threat indicators View your threat indicators with KQL 36 - Connect data to Microsoft Sentinel using data connectors Ingest log data with data connectors Understand data connector providers View connected hosts 37 - Connect Microsoft services to Microsoft Sentinel Plan for Microsoft services connectors Connect the Microsoft Office 365 connector Connect the Microsoft Entra connector Connect the Microsoft Entra ID Protection connector Connect the Azure Activity connector 38 - Connect Microsoft Defender XDR to Microsoft Sentinel Plan for Microsoft Defender XDR connectors Connect the Microsoft Defender XDR connector Connect Microsoft Defender for Cloud connector Connect Microsoft Defender for IoT Connect Microsoft Defender legacy connectors 39 - Connect Windows hosts to Microsoft Sentinel Plan for Windows hosts security events connector Connect using the Windows Security Events via AMA Connector Connect using the Security Events via Legacy Agent Connector Collect Sysmon event logs 40 - Connect Common Event Format logs to Microsoft Sentinel Plan for Common Event Format connector Connect your external solution using the Common Event Format connector 41 - Connect syslog data sources to Microsoft Sentinel Plan for syslog data collection Collect data from Linux-based sources using syslog Configure the Data Collection Rule for Syslog Data Sources Parse syslog data with KQL 42 - Connect threat indicators to Microsoft Sentinel Plan for threat intelligence connectors Connect the threat intelligence TAXII connector Connect the threat intelligence platforms connector View your threat indicators with KQL 43 - Threat detection with Microsoft Sentinel analytics What is Microsoft Sentinel Analytics? Types of analytics rules Create an analytics rule from templates Create an analytics rule from wizard Manage analytics rules 44 - Automation in Microsoft Sentinel Understand automation options Create automation rules 45 - Threat response with Microsoft Sentinel playbooks What are Microsoft Sentinel playbooks? Trigger a playbook in real-time Run playbooks on demand 46 - Security incident management in Microsoft Sentinel Understand incidents Incident evidence and entities Incident management 47 - Identify threats with Behavioral Analytics Understand behavioral analytics Explore entities Display entity behavior information Use Anomaly detection analytical rule templates 48 - Data normalization in Microsoft Sentinel Understand data normalization Use ASIM Parsers Understand parameterized KQL functions Create an ASIM Parser Configure Azure Monitor Data Collection Rules 49 - Query, visualize, and monitor data in Microsoft Sentinel Monitor and visualize data Query data using Kusto Query Language Use default Microsoft Sentinel Workbooks Create a new Microsoft Sentinel Workbook 50 - Manage content in Microsoft Sentinel Use solutions from the content hub Use repositories for deployment 51 - Explain threat hunting concepts in Microsoft Sentinel Understand cybersecurity threat hunts Develop a hypothesis Explore MITRE ATT&CK 52 - Threat hunting with Microsoft Sentinel Explore creation and management of threat-hunting queries Save key findings with bookmarks Observe threats over time with livestream 53 - Use Search jobs in Microsoft Sentinel Hunt with a Search Job Restore historical data 54 - Hunt for threats using notebooks in Microsoft Sentinel Access Azure Sentinel data with external tools Hunt with notebooks Create a notebook Explore notebook code

Duration 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

RQF level 1 Awareness of First Aid for Mental Health Each year approximately 1 in 4 people in the UK will experience a mental health condition and at least 1 in 6 employees experience common mental health problems in the workplace. Research has shown that work is the biggest cause of stress which can stop people performing at their best. Mental health conditions are often hidden due to stigma and fear of discrimination and research has shown that a culture of fear and silence around mental health is costly to employers. The HSE guidance 'First aid needs assessment’ refers to mental health in the workplace. This 4-hour qualification provides learners with the knowledge to recognise a range of mental health conditions, how to start a supportive conversation and when and how to signpost a person to seek appropriate professional help. Learners will know how to recognise and manage stress. Learners will not diagnose or treat mental health conditions as this can only be carried out by healthcare professionals but will gain the knowledge to identify when a person may have a condition and know where they can go to get help. Suitability - Who should attend? Here are some examples of who may benefit from attending the RQF Level 1 Award in Awareness of First Aid for Mental Health: Employees and workers: This course is relevant for individuals in any industry who may encounter colleagues or clients experiencing mental health difficulties. It can be particularly valuable for human resources personnel, line managers, supervisors, or team leaders responsible for the well-being of employees. Teachers and educators: Professionals working in schools, colleges, or other educational institutions can benefit from this training to better understand and support the mental health needs of students. Healthcare and social care workers: Individuals working in healthcare or social care settings, such as nurses, care assistants, support workers, or counsellors, can enhance their understanding of mental health issues and improve their ability to provide appropriate support. Community and voluntary workers: People involved in community or voluntary organizations, including youth workers, social workers, volunteers, or community leaders, can gain valuable insights into mental health awareness and support. Personal relationships and caregivers: The Level 1 training can also be beneficial for individuals who have personal relationships with someone experiencing mental health challenges. This may include family members, friends, or caregivers who want to enhance their understanding and offer appropriate assistance. It is important to note that the Level 1 Award in Awareness of First Aid for Mental Health RQF is an introductory course and does not qualify participants to provide formal mental health interventions or therapy. However, it serves as a foundation for further training and can contribute to creating a more mentally healthy and supportive environment in various settings. Outcome / Qualification etc. Upon successful completion of the RQF Level 1 Awareness of First Aid for Mental Health course, participants can expect to achieve the following outcomes: Increased Awareness and Understanding: Participants will develop a basic awareness and understanding of mental health and mental health issues. They will gain knowledge about common mental health conditions, their signs and symptoms, and the importance of mental health in overall well-being. Recognition of Mental Health Signs: Participants will learn to recognize common signs of mental health issues in themselves and others. They will gain an understanding of the importance of early identification and intervention in promoting mental health and seeking appropriate support. Reduced Stigma and Improved Attitudes: The course aims to challenge stigmas and stereotypes associated with mental health. Participants will develop a more empathetic and supportive attitude towards individuals experiencing mental health challenges, promoting a positive and inclusive environment. Enhanced Communication Skills: Participants will learn basic communication skills for engaging with individuals experiencing mental health issues. They will gain an understanding of the importance of active listening, empathy, and non-judgmental communication in providing initial support. Signposting and Seeking Help: Participants will be equipped with knowledge about available resources, services, and support networks for mental health. They will learn about signposting individuals to appropriate professional help and self-help resources. Self-Care and Well-being Strategies: The course may provide participants with practical strategies for maintaining their own mental well-being. They may learn basic self-care techniques and stress management strategies to support their own mental health. Certificate of Completion: Upon successful completion of the course, participants will receive a certificate indicating their achievement of the RQF Level 1 Awareness of First Aid for Mental Health qualification. It's important to note that the Level 1 course provides a basic introduction to mental health awareness and first aid. It is not intended to provide participants with the qualifications or skills to diagnose or treat mental health conditions. Instead, it aims to promote mental health literacy, reduce stigma, and provide individuals with the knowledge to offer initial support and signposting to individuals in need. The Level 1 course can serve as a foundation for further learning and progression in the field of mental health. Individuals may choose to pursue higher-level courses or qualifications to develop more advanced skills and knowledge in mental health first aid or other related areas. Training Course Content MODULE 1 INTRODUCTION Session content Trainer/assessor introduction Learner introductions Course information • Administration • Learning outcomes and assessment criteria • Reasonable adjustments • Certification • Complaints and appeals • Assessment information Session duration 15 minutes MODULE 2 WHAT IS FIRST AID FOR MENTAL HEALTH? Session content Definitions Role and responsibilities of a First Aider The impact of mental health issues Mental health stigma Statistics Session duration 25 minutes MODULE 3 IDENTIFYING MENTAL HEALTH CONDITIONS Session content Mental health continuum Mental health risk factors Early warning signs Session duration 25 minutes MODULE 4 PROVIDING ADVICE AND STARTING A CONVERSATION Session content How to start a difficult conversation Non-judgemental listening skills When to contact the emergency services The First Aider’s own health and emotions Session duration 40 minutes MODULE 5 STRESS Session content What is stress? Causes of stress Effects of stress Coping strategies Session duration 25 minutes MODULE 6 MENTAL HEALTH CONDITIONS Session content Depression Anxiety Psychosis Eating disorders Suicide Self-harm Session duration 70 minutes MODULE 7 ASSESSMENT AND COURSE CLOSURE Session content Written assessment paper Course administration Course closure Session duration 40 minute Course delivery details Classroom-Based A minimum of 4 hours spread over at least one day. Ideally, the course should be run in one day, but must be completed within 2 weeks of starting the course, with each training session a minimum of two hours. Online/Virtual classroom The qualification has 2 assigned guided learning hours (GLH) and 5 hours total qualification time (TQT). GLH indicates the number of contact hours that the learner will have with the trainer/assessor. TQT includes GLH but considers unsupervised learning and is an estimate of how long the average learner will take to achieve the qualification. Why choose Madeleys First Aid Plus Founded in 2021 after Louise left 30 years in the NHS as an Advanced practitioner in A&E/ITU, had spent 1.5 years in Covid ITU Won FSB Best start-up business in the West Midlands in May 2023 Now trained 100's of delegates in Physical and Mental Health First Aid Expenses Travel costs and lunch required, there are many cafes and sandwich bars here in Much Wenlock to buy your lunch, you may eat it in the training room. All training material, books, qualification certificates are included in the price. Continuing Studies After completing the RQF Level 1 Awareness of First Aid for Mental Health course, individuals can consider various progression options to further their knowledge and skills in mental health support. Here are some potential pathways: RQF Level 2 Award in First Aid for Mental Health: This qualification builds upon the knowledge gained in the Level 1 course and provides a more comprehensive understanding of mental health issues and how to provide appropriate support. It covers topics such as recognizing mental health conditions, promoting well-being, and providing initial support to those in crisis. RQF Level 3 Award in Supervising First Aid for Mental Health: For individuals who aspire to take on leadership or supervisory roles in mental health support, the Level 3 qualification is a logical progression. It provides in-depth knowledge and skills to supervise and manage a team of individuals providing first aid for mental health. Continued Professional Development (CPD): Engaging in ongoing CPD activities is essential for staying updated with the latest developments in mental health support. Individuals can attend workshops, seminars, or conferences related to mental health, trauma, or specific areas of interest within the field. Applied Practice: Applying the knowledge gained from the Level 1 course in real-world settings is crucial for developing practical skills. Individuals can seek opportunities to work or volunteer in environments where mental health support is needed, such as community organizations, schools, or helplines. Mental Health Support Training Programs: There are various specialized training programs available that focus on specific aspects of mental health support, such as suicide prevention, trauma-informed care, or supporting individuals with specific mental health conditions. These programs can provide individuals with additional expertise and deepen their understanding of specific areas within mental health support. Higher Education: Individuals who wish to pursue a more in-depth study of mental health can consider higher education programs in psychology, counseling, social work, or related fields. These programs provide comprehensive knowledge and training in mental health support and may lead to professional certifications or degrees. It's important for individuals to research and explore progression options that align with their specific career goals, interests, and local requirements. Different countries or regions may have varying certification or training requirements for mental health support roles, so it's advisable to check with relevant regulatory bodies or professional associations for specific guidance.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

Sage 50 Training: Overview Starting our Sage 50 Accounting courses will enhance your career potentials and give you the skills and knowledge you need to get started in Finance and Accountancy Industry. In Addition, our courses are designed to comply with AAT and Sage certification exams. Why wait, start a new direction to your career in Accountancy. According to statistics, the average salary for Accountants is over £50,000 (Source: Reed Salary Checker). In this sector, the employability rate is higher than in any other sector. Professional or Industry specific qualification

Sage 50 Courses: Overview Starting our Sage 50 Accounting courses will enhance your career potentials and give you the skills and knowledge you need to get started in Finance and Accountancy Industry. In Addition, our courses are designed to comply with AAT and Sage certification exams. Why wait, start a new direction to your career in Accountancy. According to statistics, the average salary for Accountants is over £50,000 (Source: Reed Salary Checker). In this sector, the employability rate is higher than in any other sector. Professional or Industry specific qualification

Mental Health First Aid England Aware is an introductory course designed to increase mental health awareness and give an understanding of how to look after wellbeing and challenge stigma. Through an interactive instructor-led live session, you will learn: What mental health is and how to challenge stigma An introduction to some common mental health issues Confidence to support someone who may be experiencing mental ill health Ways to look after your own mental health and support wellbeing Outline What is mental health? Mental Health Continuum Factors that affect mental health Stigma Stress and stress management Spotting signs of distress Mental health conditions:DepressionAnxiety disordersPsychosisEating disordersSuicideSelf-harm Recovery Take 10 Together - starting a supportive conversation Supporting mental health in the workplace Useful statistics Helpful resources

The course is relevant to anyone requiring an understanding of the use of Agile or looking to adopt it. This includes, but is not limited to, organisational leaders and managers, marketing executives and managers, and/or all professionals working in an Agile environment, including software sesters, developers, business analysts, UX designers, project management office (PMO), project support and project coordinators.

Duration 2 Days 12 CPD hours This course is intended for This course is intended for network administrators, operators, and engineers responsible for managing the normal day-to-day operation and administration of a BIG-IP application delivery network. This course presents the prerequisite knowledge for many other of F5's BIG-IP instructor-led training courses. Overview Getting started with the BIG-IP system Traffic processing with BIG-IP Local Traffic Manager (LTM) Using the TMSH (TMOS Shell) command line interface Using NATs and SNATs Monitoring application health and managing object status Modifying traffic behavior with profiles, including SSL offload and re-encryption Modifying traffic behavior with persistence, including source address affinity and cookie persistence Troubleshooting the BIG-IP system, including logging (local, high-speed, and legacy remote logging), and using tcpdump User roles and administrative partitions vCMP concepts Customizing application delivery with iRules This course gives network administrators, network operators, and network engineers a functional understanding of the BIG-IP system as it is commonly deployed in an application delivery network. The course introduces students to the BIG-IP system, its configuration objects, how it processes traffic, and how typical administrative and operational activities are performed. The course includes lecture, hands-on labs, interactive demonstrations, and discussions. Setting Up the BIG-IP System Introducing the BIG-IP System Initially Setting Up the BIG-IP System Configuring the Management Interface Activating the Software License Provisioning Modules and Resources Importing a Device Certificate Specifying BIG-IP Platform Properties Configuring the Network Configuring Network Time Protocol (NTP) Servers Configuring Domain Name System (DNS) Settings Configuring High Availability Options Archiving the BIG-IP Configuration Leveraging F5 Support Resources and Tools Traffic Processing Building Blocks Identifying BIG-IP Traffic Processing Objects Configuring Virtual Servers and Pools Load Balancing Traffic Viewing Module Statistics and Logs Using the Traffic Management Shell (TMSH) Understanding the TMSH Hierarchical Structure Navigating the TMSH Hierarchy Managing BIG-IP Configuration State and Files BIG-IP System Configuration State Loading and Saving the System Configuration Shutting Down and Restarting the BIG-IP System Saving and Replicating Configuration Data (UCS and SCF) Using NATs and SNATs Address Translation on the BIG-IP System Mapping IP Addresses with NATs Solving Routing Issues with SNATs Configuring SNAT Auto Map on a Virtual Server Monitoring for and Mitigating Port Exhaustion Monitoring Application Health Introducing Monitors Types of Monitors Monitor Interval and Timeout Settings Configuring Monitors Assigning Monitors to Resources Managing Pool, Pool Member, and Node Status Using the Network Map Modifying Traffic Behavior with Profiles Introducing Profiles Understanding Profile Types and Dependencies Configuring and Assigning Profiles Introducing SSL Offload and SSL Re-Encryption Managing Object State Modifying Traffic Behavior with Persistence Understanding the Need for Persistence Introducing Source Address Affinity Persistence Managing Object State Administering the BIG-IP System Configuring Logging Legacy Remote Logging Introducing High Speed Logging (HSL) High-Speed Logging Filters HSL Configuration Objects Configuring High Speed Logging Using TCPDUMP on the BIG-IP System Leveraging the BIG-IP iHealth System Viewing BIG-IP System Statistics Defining User Roles and Administrative Partitions Leveraging vCMP Configuring High Availability Introducing Device Service Clustering (DSC) Preparing to Deploy a DSC Configuration Configuring DSC Communication Settings Establishing Device Trust Establishing a Sync-Failover Device Group Synchronizing Configuration Data Exploring Traffic Group Behavior Understanding Failover Managers and Triggers Achieving Stateful Failover with Mirroring