Duration 5 Days 30 CPD hours This course is intended for Security Professionals working with Kubernetes Clusters Container Orchestration Engineers DevOps Professionals Overview In this course, students will learn and practice essential Kubernetes concepts and tasks in the following sections: Cloud Security Fundamentals Cluster Hardening System Hardening Minimize Microservice Vulnerabilities Supply Chain Security Disaster Recovery Secure Back-up and Restore This class prepares students for the Certified Kubernetes Security Specialist (CKS) exam. Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stabilitywhile maximizing resource utilization for applications and services. By the conclusion of this hands-on, vendor agnostic training you will be equipped with a thorough understanding ofcloud security fundamentals, along with the knowledge, skills and abilities to secure a Kubernetes cluster, detect threats, and properly resolve a security catastrophe. This courseincludes hands-on instruction which develops skills and knowledge for securing container-based applications and Kubernetes platforms, during build, deployment, and runtime. We prioritizecovering all objectives and concepts necessary for passing the Certified Kubernetes Security Specialist (CKS) exam. You will be provided the components necessary to assemble your ownhigh availability Kubernetes environment and harden it for your security needs. Learning Your Environment Underlying Infrastructure Using Vim Tmux Cloud Security Primer Basic Principles Threat Analysis Approach CIS Benchmarks Securing your Kubernetes Cluster Kubernetes Architecture Pods and the Control Plane Kubernetes Security Concepts Install Kubernetes using kubeadm Configure Network Plugin Requirements Kubeadm Basic Cluster Installing Kubeadm Join Node to Cluster Kubeadm Token Manage Kubeadm Tokens Kubeadm Cluster Upgrade Securing the kube-apiserver Configuring the kube-apiserver Enable Audit Logging Falco Deploy Falco to Monitor System Calls Enable Pod Security Policies Encrypt Data at Rest Encryption Configuration Benchmark Cluster with Kube-Bench Kube-Bench Securing ETCD ETCD Isolation ETCD Disaster Recovery ETCD Snapshot and Restore Purge Kubernetes Purge Kubeadm 3Purge Kubeadm Image Scanning Container Essentials Secure Containers Creating a Docker Image Scanning with Trivy Trivy Snyk Security Manually Installing Kubernetes Kubernetes the Alta3 Way Deploy Kubernetes the Alta3 Way Validate your Kubernetes Installation Sonobuoy K8s Validation Test Kubectl (Optional) Kubectl get and sorting kubectl get kubectl describe Labels (Optional) Labels Labels and Selectors Annotations Insert an Annotation Securing your Application Scan a Running Container Tracee Security Contexts for Pods Understanding Security Contexts AppArmor Profiles AppArmor Isolate Container Kernels gVisor Pod Security Pod Security Policies Deploy a PSP Pod Security Standards Enable PSS Open Policy Agent (OPA) Admission Controller Create a LimitRange Open Policy Agent Policy as Code Deploy Gatekeeper User Administration Contexts Contexts Authentication and Authorization Role Based Access Control Role Based Access Control RBAC Distributing Access Service Accounts Limit Pod Service Accounts Securing Secrets Secrets Create and Consume Secrets Hashicorp Vault Deploy Vault Securing the Network Networking Plugins NetworkPolicy Deploy a NetworkPolicy mTLS Linkerd mTLS with istio istio Threat Detection Active Threat Analysis Host Intrusion Detection Deploy OSSEC Network Intrusion Detection Deploy Suricata Physical Intrusion Detection Disaster Recovery Harsh Reality of Security Deploy a Response Plan Kasten K10 Backups Deploy K10
Duration 1 Days 6 CPD hours This course is intended for This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks. Overview After completing this course, you will be able to: Provision an Azure Databricks workspace and cluster Use Azure Databricks to train a machine learning model Use MLflow to track experiments and manage machine learning models Integrate Azure Databricks with Azure Machine Learning Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. Introduction to Azure Databricks Getting Started with Azure Databricks Working with Data in Azure Databricks Training and Evaluating Machine Learning Models Preparing Data for Machine Learning Training a Machine Learning Model Managing Experiments and Models Using MLflow to Track Experiments Managing Models Managing Experiments and Models Using MLflow to Track Experiments Managing Models Integrating Azure Databricks and Azure Machine Learning Tracking Experiments with Azure Machine Learning Deploying Models
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The Project Manager's Value Add through Artificial Intelligence Do you know that Artificial Intelligence (AI) is all around you? Today, even small projects have more data than a project manager can effectively trend or digest. Artificial Intelligence can help you today. Implementing AI on a project and understanding how to use it effectively makes you a value add over those that do not use it. Project managers are leaders and leaders do what is best for the team. Artificial Intelligence will assist in demonstrating your value as a leader. The future of AI and projects is only bound by the imagination. What You Will Learn: How to use Artificial Intelligence on a project• It is not understanding 1s and 0s, it is understanding how to use AI How to get AI implemented on a project• AI and the future of project management
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In the ever-evolving world of production, ensuring impeccable quality is paramount. 'Product Testing Protocols for Quality Assurance' offers a comprehensive dive into the methodologies and strategies vital for ensuring stellar product quality. Starting with the rudiments of product testing, the course delves deep into varied test types, utilising tools effectively, deciphering results, and the significance of adhering to global standards. With a robust curriculum encompassing everything from failure prevention to continuous improvement, this course is a passport to mastering the nuances of quality assurance in product testing. Learning Outcomes Understand the foundational principles of product testing. Identify and differentiate between various types of product tests. Master the art of designing and conducting product tests efficiently. Analyse and interpret testing outcomes to drive product quality. Recognise the importance of compliance, and adopt strategies for ongoing enhancement in product testing. Why buy this Product Testing Protocols for Quality Assurance? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Product Testing Protocols for Quality Assurance for? Quality assurance analysts and enthusiasts. Manufacturers keen on refining their product quality. Product developers aiming for benchmark quality standards. Team leaders responsible for product validation. Start-up founders launching new products and wishing to ensure top-notch quality. Career path Quality Assurance Analyst: £25,000 - £40,000 Product Testing Engineer: £30,000 - £50,000 Compliance Officer: £28,000 - £45,000 Quality Control Manager: £35,000 - £60,000 Product Development Specialist: £32,000 - £55,000 Continuous Improvement Manager: £40,000 - £70,000 Prerequisites This Product Testing Protocols for Quality Assurance does not require you to have any prior qualifications or experience. You can just enrol and start learning. This course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 1: Introduction to Product Testing Introduction to Product Testing 00:11:00 Module 2: Types of Product Tests Types of Product Tests 00:16:00 Module 3: Planning and Executing Product Tests Planning and Executing Product Tests 00:15:00 Module 4: Testing Tools and Equipment Testing Tools and Equipment 00:10:00 Module 5: Interpreting Test Results Interpreting Test Results 00:11:00 Module 6: Compliance and Standards Compliance and Standards 00:09:00 Module 7: Preventing Failure and Surprises Preventing Failure and Surprises 00:14:00 Module 8: Communicating Test Results Communicating Test Results 00:18:00 Module 9: Continuous Improvement Through Testing Continuous Improvement Through Testing 00:10:00
Dive into the digital realm with our 'Basic HTML 5' course. Engage with an exciting curriculum crafted meticulously to guide learners through the foundational concepts, starting with the rudiments of HTML and culminating with intricate CSS implementations. Whether you aim to mould webpages or breathe life into them with stunning designs, our content-rich modules unravel the secrets of web development in a structured manner. Learning Outcomes Understand the foundational principles of HTML and its significance in web development. Identify and employ various layout and inline elements effectively. Differentiate and utilise specific elements tailored for specific tasks. Grasp the essentials of CSS, laying the groundwork for advanced styling techniques. Implement and customise navigation styles, mastering the art of web aesthetics. Why buy this Basic HTML 5 course? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Basic HTML 5 there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this Basic HTML 5 course for? Beginners eager to embark on their web development journey. Design enthusiasts keen to integrate HTML with aesthetics. Bloggers and content creators aiming for a personalised web presence. Tech aficionados looking to understand web design basics. Entrepreneurs wishing to gain insights into website structuring. Prerequisites This Basic HTML 5 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Basic HTML 5 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Web Developer: £30,000 - £50,000 Front-end Developer: £28,000 - £48,000 UI Designer: £32,000 - £52,000 Content Strategist: £29,000 - £49,000 Website Tester: £27,000 - £47,000 Digital Marketer: £26,000 - £46,000 Course Curriculum Basic HTML 5 Section 01: HTML Introduction 00:04:00 Section 02: Layout and Elements 00:09:00 Section 03: Inline Elements 00:07:00 Section 04: Specific Elements 00:05:00 Section 05: CSS Introduction 00:02:00 Section 06: CSS Implementation 00:07:00 Section 07: CSS Elements 00:03:00 Section 08: Borders and Backgrounds 00:10:00 Section 09: Navigation Styling 00:07:00 Section 10: Tips and Tricks 00:06:00 Assignment Assignment - Basic HTML 5 00:00:00
Embark on a Coding Odyssey: Unleash Your Potential with Embedded C Programming Course Dive into the heart of programming as we unveil the mysteries of Embedded C in our comprehensive course designed for both novices and coding enthusiasts. 'Embedded C Programming Course' is your passport to a world of limitless possibilities. From mastering the basics of C to navigating conditional executions, loops, arrays, pointers, and functions, this course is your roadmap to fluency in the language of coding. Our interactive modules, led by seasoned instructors, ensure that each concept is not just understood but absorbed, making your coding journey both engaging and effective. Learning Outcomes Acquire a solid foundation in C programming, from installing it on your computer to understanding characters and integers. Navigate conditional executions and loops with confidence, mastering if conditions, switch cases, for loops, and while loops. Delve into the intricacies of arrays, including creation, manipulation, and working with character arrays. Unlock the power of pointers, comprehending their role and functionality in the coding realm. Master the art of using functions, including global variables, returning values via pointers, and efficiently passing arrays. Why choose this Embedded C Programming Course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Embedded C Programming Course for? Aspiring programmers eager to delve into the world of embedded systems. Computer science students seeking a practical understanding of C programming. Tech enthusiasts looking to enhance their coding skills. Professionals aiming to pivot into embedded systems development. Individuals curious about the mechanics of conditional executions, loops, arrays, pointers, and functions in C programming. Career path Embedded Systems Developer: £30,000 - £60,000 C Programmer: £25,000 - £45,000 Software Engineer: £35,000 - £65,000 Firmware Developer: £40,000 - £70,000 Systems Architect: £45,000 - £75,000 Technical Consultant: £50,000 - £80,000 Prerequisites This Embedded C Programming Course does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Embedded C Programming Course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Getting Started with C Install C on your computer 00:08:00 Addition of Numbers 00:10:00 Accepting Inputs with Scanf in C 00:05:00 Understanding Characters and Int in C 00:13:00 Division and MOD operators in C 00:06:00 Conditional Executions and Loops If Condition: Part 1 00:12:00 If Condition: Part 2 00:04:00 If Condition: Part 3 00:10:00 Switch Case 00:13:00 For Loop in C: Part 1 00:10:00 For Loop in C: Part 2 00:06:00 While Loop 00:08:00 Arrays in C Arrays: Part 1 00:12:00 Arrays: Part 2 00:09:00 Array Programs: Part 1 00:08:00 Array Programs: Part 2 00:12:00 Character Array in C 00:14:00 Character Array in C: Part 2 00:13:00 Character Array in C: Part 3 00:09:00 Convert int to String Array 00:11:00 Pointers and Functions Pointers: Part 1 00:17:00 Pointers: Part 2 00:09:00 Pointers: Part 3 00:08:00 Functions: Part 1 00:09:00 Functions: Part 2 00:09:00 Functions: Part 3 00:11:00 Functions: Part 4 00:10:00 Functions: Returning values via pointers 00:12:00 Functions: Global Variables and usage with functions 00:13:00 Passing Array to functions 00:06:00 Passing Char array (strings) to Functions 00:16:00 Downloadable Resources Resource - Embedded C Programming Course 00:00:00
Do you want to master the essential mathematical skills for data science and machine learning? Do you want to learn how to apply statistics and probability to real-world problems and scenarios? If yes, then this course is for you! In this course, you will learn the advanced concepts and techniques of statistics and probability that are widely used in data science and machine learning. You will learn how to describe and analyse data using descriptive statistics, distributions, and probability theory. You will also learn how to perform hypothesis testing, regressions, ANOVA, and machine learning algorithms to make predictions and inferences from data. You will gain hands-on experience with practical exercises and projects using Python and R. Learning Outcomes By the end of this course, you will be able to: Apply descriptive statistics, distributions, and probability theory to summarise and visualise data Perform hypothesis testing, regressions, ANOVA, and machine learning algorithms to make predictions and inferences from data Use Python and R to implement statistical and machine learning methods Interpret and communicate the results of your analysis using appropriate metrics and visualisations Solve real-world problems and scenarios using statistics and probability Why choose this Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 course for? This course is for anyone who wants to learn the advanced concepts and techniques of statistics and probability for data science and machine learning. This course is suitable for: Data scientists, machine learning engineers, and analysts who want to enhance their skills and knowledge Students and researchers who want to learn the mathematical foundations of data science and machine learning Professionals and managers who want to understand and apply data-driven decision making Hobbyists and enthusiasts who want to explore and learn from data Anyone who loves statistics and probability and wants to challenge themselves Career path Data Scientist (£35,000 - £55,000) Machine Learning Engineer (£40,000 - £60,000) Statistician (£35,000 - £55,000) Data Analyst (£40,000 - £60,000) Business Intelligence Analyst (£45,000 - £65,000) Senior Data Analyst (£50,000 - £70,000) Prerequisites This Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £135 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Section 01: Let's get started Welcome! 00:02:00 What will you learn in this course? 00:06:00 How can you get the most out of it? 00:06:00 Section 02: Descriptive statistics Intro 00:03:00 Mean 00:06:00 Median 00:05:00 Mode 00:04:00 Mean or Median? 00:08:00 Skewness 00:08:00 Practice: Skewness 00:01:00 Solution: Skewness 00:03:00 Range & IQR 00:10:00 Sample vs. Population 00:05:00 Variance & Standard deviation 00:11:00 Impact of Scaling & Shifting 00:19:00 Statistical moments 00:06:00 Section 03: Distributions What is a distribution? 00:10:00 Normal distribution 00:09:00 Z-Scores 00:13:00 Practice: Normal distribution 00:04:00 Solution: Normal distribution 00:07:00 Section 04: Probability theory Intro 00:01:00 Probability Basics 00:10:00 Calculating simple Probabilities 00:05:00 Practice: Simple Probabilities 00:01:00 Quick solution: Simple Probabilities 00:01:00 Detailed solution: Simple Probabilities 00:06:00 Rule of addition 00:13:00 Practice: Rule of addition 00:02:00 Quick solution: Rule of addition 00:01:00 Detailed solution: Rule of addition 00:07:00 Rule of multiplication 00:11:00 Practice: Rule of multiplication 00:01:00 Solution: Rule of multiplication 00:03:00 Bayes Theorem 00:10:00 Bayes Theorem - Practical example 00:07:00 Expected value 00:11:00 Practice: Expected value 00:01:00 Solution: Expected value 00:03:00 Law of Large Numbers 00:08:00 Central Limit Theorem - Theory 00:10:00 Central Limit Theorem - Intuition 00:08:00 Central Limit Theorem - Challenge 00:11:00 Central Limit Theorem - Exercise 00:02:00 Central Limit Theorem - Solution 00:14:00 Binomial distribution 00:16:00 Poisson distribution 00:17:00 Real life problems 00:15:00 Section 05: Hypothesis testing Intro 00:01:00 What is a hypothesis? 00:19:00 Significance level and p-value 00:06:00 Type I and Type II errors 00:05:00 Confidence intervals and margin of error 00:15:00 Excursion: Calculating sample size & power 00:11:00 Performing the hypothesis test 00:20:00 Practice: Hypothesis test 00:01:00 Solution: Hypothesis test 00:06:00 T-test and t-distribution 00:13:00 Proportion testing 00:10:00 Important p-z pairs 00:08:00 Section 06: Regressions Intro 00:02:00 Linear Regression 00:11:00 Correlation coefficient 00:10:00 Practice: Correlation 00:02:00 Solution: Correlation 00:08:00 Practice: Linear Regression 00:01:00 Solution: Linear Regression 00:07:00 Residual, MSE & MAE 00:08:00 Practice: MSE & MAE 00:01:00 Solution: MSE & MAE 00:03:00 Coefficient of determination 00:12:00 Root Mean Square Error 00:06:00 Practice: RMSE 00:01:00 Solution: RMSE 00:02:00 Section 07: Advanced regression & machine learning algorithms Multiple Linear Regression 00:16:00 Overfitting 00:05:00 Polynomial Regression 00:13:00 Logistic Regression 00:09:00 Decision Trees 00:21:00 Regression Trees 00:14:00 Random Forests 00:13:00 Dealing with missing data 00:10:00 Section 08: ANOVA (Analysis of Variance) ANOVA - Basics & Assumptions 00:06:00 One-way ANOVA 00:12:00 F-Distribution 00:10:00 Two-way ANOVA - Sum of Squares 00:16:00 Two-way ANOVA - F-ratio & conclusions 00:11:00 Section 09: Wrap up Wrap up 00:01:00 Assignment Assignment - Statistics & Probability for Data Science & Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00