451 Algorithm courses

Machine learning doesn’t need to be intimidating—especially when you’ve got R on your side. This course offers a clear, well-paced approach to learning machine learning using one of the most respected languages in data science. Whether you’re brushing up on your statistics or stepping into data modelling, the content is structured to help you think algorithmically and act analytically, without feeling overwhelmed by jargon or complexity. From regression techniques to classification methods and everything in-between, this course covers the core building blocks that give machine learning its predictive power. R is not just a programming language here—it’s your analytical toolkit. If terms like decision trees, clustering, and support vector machines sound like something out of a sci-fi novel, don’t worry—by the end, they’ll feel like familiar companions. Whether you’re analysing patterns or building predictive models, this course offers a confident route through the world of machine learning with an R-flavoured lens. Ask ChatGPT Learning Outcomes: Understand the basics of machine learning and its implementation using R. Develop the skills to build simple and multiple linear regression models. Learn how to use R to analyse datasets and develop predictive models. Understand the concept of dummy variables and the backward elimination approach. Learn how to make accurate predictions using machine learning algorithms and extract valuable insights from data. If you're looking to expand your knowledge in data analysis and machine learning, then the "Learn Machine Learning with R" course is perfect for you. This comprehensive course comprises two sections, each designed to help you gain an in-depth understanding of machine learning concepts, starting from the very basics. You'll learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. Additionally, you'll dive into multiple linear regression, dummy variable concepts, and predictions over the year. With the help of this course, you'll be able to analyse datasets, develop predictive models, and extract valuable insights from them, using R. Learn Machine Learning with R Course Curriculum Section 01: Linear Regression and Logistic Regression Working on Linear Regression Equation Making the Regression of the Algorithm Basic Types of Algorithms predicting the Salary of the Employee Making of Simple Linear Regression Model Plotting Training Set and Work Section 02: Understanding Dataset Multiple Linear Regression Dummy Variable Concept Predictions Over Year Difference Between Reference Elimination Working of the Model Working on Another Dataset Backward Elimination Approach Making of the Model with Full and Null How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Students or professionals looking to develop their data analysis and machine learning skills. Individuals interested in pursuing a career in data science or machine learning. Anyone interested in understanding how to extract insights from data. Programmers looking to learn machine learning implementation using R. Beginners interested in learning the basics of machine learning. Career path Data analyst: £30,000 to £50,000 Machine learning engineer: £45,000 to £85,000 Data scientist: £40,000 to £80,000 Business analyst: £30,000 to £55,000 Research analyst: £25,000 to £45,000 Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

MEF Carrier Ethernet training course description The course progresses from a overview of the Carrier Ethernet service and how it works onto looking at the concepts in depth. Service attributes and management follow with the course finishing with studies of practical Carrier Ethernet. What will you learn Discuss and understand key Carrier Ethernet Concepts. Understand tasks related to designing, deploying and maintaining a Carrier Ethernet network. Offer effective solutions to implementing a Carrier Ethernet enterprise network given available customer resources and requirements. Carry out informed discussions using industry Carrier Ethernet 'vocabulary. Pass the MEF CECP 2.0 professional accreditation exam. MEF Carrier Ethernet training course details Who will benefit: Anyone working with Carrier Ethernet Prerequisites: The course attendees need to be conversant with data networks, as well as Ethernet and IP technologies. Duration 5 days MEF Carrier Ethernet training course contents Section One: Introduction to Carrier Ethernet Introduction to Carrier Ethernet: What is Carrier Ethernet? Evolution, advantages, The MEF, MEF specifications; UNI, EVC, OVC, EPL/EVPL, EP-LAN/ EVP-LAN, EP-Tree/EVP-Tree, etc, overview. How Carrier Ethernet Works: Service Frame Handling. Carrier Ethernet at Customer Premises, metro and core. Carrier Ethernet Workings, UNI attributes, Service Attributes (EVC and EVC per UNI attributes), Bandwidth Profiles, service multiplexing, L2 protocol processing; Carrier Ethernet equipment, CPE, aggregation and homing nodes, core equipment; management systems. The Setting Up of a Carrier Ethernet Service: Step 1: Choose service type, EPL/EVPL, EP-LAN/EVP-LAN, EPTree/EVP-Tree, EVLine...; Step 2: CPE tasks, UNI-C tasks (UNI attributes, service attributes (EVC and EVC per UNI) and bandwidth profiles), UNI-N tasks (L2 protocol handling). Step 3: Non-CPE tasks, Access, metro and core connections set up. Section Two: Carrier Ethernet Concepts in depth Carrier Ethernet Definitions in Depth: UNI, UNI I & II, UNI-N and UNI-C, etc.; NNI/ENNI; EVC; OVC, OVC type (P2P, M2M, Rooted MP), OVC end point (root, leaf, trunk), OVC end point map, OVC end point bundling; Service types in detail, EPL/EVPL, EP-LAN/EVP-LAN, EP-Tree/EVP-Tree, EVLine, Access EPL, Access EVPL . Carrier Ethernet Service Frame Handling: Unicast, multicast and broadcast frame delivery, Tagged, untagged and priority; Tagging, C and S-Tags, 802.3, 802.1d, 802.1q, 802.1ad, 802.1ah evolution, VLAN ID translation/preservation. CoS preservation. Other Key Carrier Ethernet Concepts: MTU, MTU at UNI, MTU at ENNI; Physical Layer Attributes, FE, GbE and 10GbE, Service Multiplexing and Bundling Concept and detail, rules and implications; Hairpin Switching Managing Bandwidth in a Carrier Ethernet Network: Token Bucket Algorithm, EIR, CIR, CBS, EBS, Coupling Flag; Frame Colors, recoloring, Color Awareness attribute, Color Forwarding; Bandwidth Profiles, rules and concepts. MEF CoS identifiers, DEI bit (in S-Tag), PCP bit (in C-Tag or S-Tag), or DSCP (in IP header), Multiflow bandwidth concepts; CoS Label/Color Identification. Section Three: Carrier Ethernet Service Attributes Overview: Carrier Ethernet 2.0; Blueprint C Service Attributes: Per UNI, Physical interfaces, Frame format, Ingress/egress Bandwidth Profiles, CEVLAN ID/EVC Map, UNI protection. EVC per UNI, Ingress/egress Bandwidth Profiles, etc.; Per EVC, CEVLAN ID Preservation, CoS ID Preservation, Relationship between SLA and SLP, Class of Service, etc. OVC, ENNI, OVC End Point per UNI and OVC End Point per ENNI, Ingress/egress bandwidth profiles, etc. Section Four: Managing Carrier Ethernet Networks Overview: MEF Service Lifecycle.Carrier Ethernet maintenance: Port, Link & NE failure, Service Protection Technologies, Fault Identification and Recovery, LAG, Active/Standby EVC, Single EVC with transport protection, G.8031, G.8032, MPLS FRR. SOAMs: Connectivity fault management, connectivity Monitoring, Loopback, Linktrace; Performance Management, Frame Delay, Inter Frame Delay Variation, Availability, Frame Loss Ratio, Resiliency, HLI, DMM, DMR, SLM, SLR; Key Concepts, Single vs dual ended, ordered UNI pair calculations. LOAMs: Link discovery, link monitoring, etc. Terminology and Concepts: MEG levels, MIPs. Section Five: Practical Carrier Ethernet Carrier Ethernet Transport Technologies:Layer 1: SDH. Layer 2: Bridging, provider bridging, PBB, PBBTE. Layer 2.5: MPLS VPWS, MPLS VPLS, MPLS-TP. Carrier Ethernet Access Technologies: fiber, SDH, active fiber, PON, GPON, 10G PON, OTN, WDM; copper, PDH, G-SDSL, 10Pass-TS, HFC; packet radio. Optimising mobile backhaul with Carrier Ethernet Key challenges solutions: Market pressure, LTE evolution, elements and architecture (RAN BS, NC, GWIF.), synchronization, bandwidth management. Circuit Emulation over Ethernet: Purpose, needs and applications. Synchronization: Phased, ToD, External Reference source, SynchE ,NTP, IEEE-1588 v2/ PTP, ACR; MEF Service Definitions for emulated circuits. Applying what you know: Practical examples and scenarios, Carrier Ethernet solutions; Practice Scenarios, Given a scenario, determine appropriate Ethernet services

Duration 5 Days 30 CPD hours This course is intended for This course is targeted towards the information technology (IT) professional that has a minimum 1 year IT Security and Networking experience. This course would be ideal for Information System Owners, Security Officers, Ethical Hackers, Information Owners, Penetration Testers, System Owner and Managers as well as Cyber Security Engineers. Overview Upon completion, the Certified Professional Ethical Hacker candidate will be able to competently take the CPEH exam. The CPEH certification training enables students to understand the importance of vulnerability assessments and how to implement counter response along with preventative measures when it comes to a network hack. Security Fundamentals Overview The Growth of Environments and Security Our Motivation? The Goal: Protecting Information! CIA Triad in Detail Approach Security Holistically Security Definitions Definitions Relationships Method: Ping The TCP/IP Stack Which Services Use Which Ports? TCP 3-Way Handshake TCP Flags Malware Types of Malware Types of Malware Cont... Types of Viruses More Malware: Spyware Trojan Horses Back Doors DoS DDoS Packet Sniffers Passive Sniffing Active Sniffing Firewalls, IDS and IPS Firewall ? First Line of Defense IDS ? Second Line of Defense IPS ? Last Line of Defense? Firewalls Firewall Types: (1) Packet Filtering Firewall Types: (2) Proxy Firewalls Firewall Types ? Circuit-Level Proxy Firewall Type of Circuit- Level Proxy ? SOCKS Firewall Types ? Application-Layer Proxy Firewall Types: (3) Stateful Firewall Types: (4) Dynamic Packet-Filtering Firewall Types: (5) Kernel Proxies Firewall Placement Firewall Architecture Types ? Screened Host Multi- or Dual-Homed Screened Subnet Wi-Fi Network Types Wi-Fi Network Types Widely Deployed Standards Standards Comparison 802.11n - MIMO Overview of Database Server Review Access Controls Overview Role of Access Control Definitions More Definitions Categories of Access Controls Physical Controls Logical Controls ?Soft? Controls Security Roles Steps to Granting Access Access Criteria Physical Access Control Mechanisms Biometric System Types Synchronous Token Asynchronous Token Device Memory Cards Smart Card Cryptographic Keys Logical Access Controls OS Access Controls Linux Access Controls Accounts and Groups Password & Shadow File Formats Accounts and Groups Linux and UNIX Permissions Set UID Programs Trust Relationships Review Protocols Protocols Overview OSI ? Application Layer OSI ? Presentation Layer OSI ? Session Layer Transport Layer OSI ? Network Layer OSI ? Data Link OSI ? Physical Layer Protocols at Each OSI Model Layer TCP/IP Suite Port and Protocol Relationship Conceptual Use of Ports UDP versus TCP Protocols ? ARP Protocols ? ICMP Network Service ? DNS SSH Security Protocol SSH Protocols ? SNMP Protocols ? SMTP Packet Sniffers Example Packet Sniffers Review Cryptography Overview Introduction Encryption Cryptographic Definitions Encryption Algorithm Implementation Symmetric Encryption Symmetric Downfalls Symmetric Algorithms Crack Times Asymmetric Encryption Public Key Cryptography Advantages Asymmetric Algorithm Disadvantages Asymmetric Algorithm Examples Key Exchange Symmetric versus Asymmetric Using the Algorithm Types Together Instructor Demonstration Hashing Common Hash Algorithms Birthday Attack Example of a Birthday Attack Generic Hash Demo Instructor Demonstration Security Issues in Hashing Hash Collisions MD5 Collision Creates Rogue Certificate Authority Hybrid Encryption Digital Signatures SSL/TLS SSL Connection Setup SSL Hybrid Encryption SSH IPSec - Network Layer Protection IPSec IPSec Public Key Infrastructure Quantum Cryptography Attack Vectors Network Attacks More Attacks (Cryptanalysis) Review Why Vulnerability Assessments? Overview What is a Vulnerability Assessment? Vulnerability Assessment Benefits of a Vulnerability Assessment What are Vulnerabilities? Security Vulnerability Life Cycle Compliance and Project Scoping The Project Overview Statement Project Overview Statement Assessing Current Network Concerns Vulnerabilities in Networks More Concerns Network Vulnerability Assessment Methodology Network Vulnerability Assessment Methodology Phase I: Data Collection Phase II: Interviews, Information Reviews, and Hands-On Investigation Phase III: Analysis Analysis cont. Risk Management Why Is Risk Management Difficult? Risk Analysis Objectives Putting Together the Team and Components What Is the Value of an Asset? Examples of Some Vulnerabilities that Are Not Always Obvious Categorizing Risks Some Examples of Types of Losses Different Approaches to Analysis Who Uses What? Qualitative Analysis Steps Quantitative Analysis ALE Values Uses ALE Example ARO Values and Their Meaning ALE Calculation Can a Purely Quantitative Analysis Be Accomplished? Comparing Cost and Benefit Countermeasure Criteria Calculating Cost/Benefit Cost of a Countermeasure Can You Get Rid of All Risk? Management?s Response to Identified Risks Liability of Actions Policy Review (Top-Down) Methodology Definitions Policy Types Policies with Different Goals Industry Best Practice Standards Components that Support the Security Policy Policy Contents When Critiquing a Policy Technical (Bottom-Up) Methodology Review Vulnerability Tools of the Trade Vulnerability Scanners Nessus SAINT ? Sample Report Tool: Retina Qualys Guard http://www.qualys.com/products/overview/ Tool: LANguard Microsoft Baseline Analyzer MBSA Scan Report Dealing with Assessment Results Patch Management Options Review Output Analysis and Reports Overview Staying Abreast: Security Alerts Vulnerability Research Sites Nessus SAINT SAINT Reports GFI Languard GFI Reports MBSA MBSA Reports Review Reconnaissance, Enumeration & Scanning Reconnaissance Overview Step One in the Hacking ?Life-Cycle? What Information is Gathered by the Hacker? Passive vs. Active Reconnaissance Footprinting Defined Social Access Social Engineering Techniques Social Networking Sites People Search Engines Internet Archive: The WayBack Machine Footprinting Tools Overview Maltego GUI Johnny.Ihackstuff.com Google (cont.) Domain Name Registration WHOIS Output DNS Databases Using Nslookup Traceroute Operation Web Server Info Tool: Netcraft Introduction to Port Scanning Which Services use Which Ports? Port Scan Tips Port Scans Shou

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune

Description: Are you interested in a career in computer science? Programming is the art of writing useful, maintainable, and extensible source codes which can be read or compiled by a computer system to perform a significant task. Take your first step towards learning core programming concepts and equip yourself with the practical knowledge and skills to resolve complicated problems. Discover all you need to know about programming language with this computer science course. By learning the correct programming theory, you will be able to analyse a problem and identify suitable solutions to those problems, which is a key part of web development. Apart from the theories of Algorithm analysis, this computer programming course also teaches the number system, arrays and their advantages, the process of analysing a problem, nodes and their Importance, and various sorting algorithms and their comparisons. There are no entry requirements for this course and you can study from the comfort of your own home. Enrol in this Diploma in Computer Science and Programming course today and learn to write code like an expert. Who is the course for? Anyone who wants to become a Good Programmer Anyone interested in the Computer Science Discipline Anyone who wants to learn how to problem solve like a Computer Scientist Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Assessment: At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam, you will have the opportunity to test your proficiency with a mock exam. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £14.99 or in PDF format at a cost of £11.99. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognised accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path: After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Introduction Kurt Anderson - Promo FREE 00:02:00 Kurt Anderson - 1 Introduction 00:01:00 Kurt Anderson - 2 Binary System 00:11:00 Analyzing Algorithms Kurt Anderson - 3 Complexity Introduction 00:02:00 Kurt Anderson - 4 Math Refresher Logarithmic Functions 00:11:00 Kurt Anderson - 5 Math Refresher Factorial Functions.TS 00:03:00 Kurt Anderson - 6 Math Refresher Algebraic Expressions.TS 00:03:00 Kurt Anderson - 7 n-notation 00:18:00 Kurt Anderson - 8 Big O 00:13:00 Kurt Anderson - 9 Big O Real World Example 00:10:00 Arrays Kurt Anderson - 10 How is Data Stored 00:09:00 Kurt Anderson - 11 Fixed Arrays 00:20:00 Kurt Anderson - 12 Circular Arrays 00:08:00 Kurt Anderson - 13 Dynamic Arrays 00:16:00 Kurt Anderson - 14 Array Review 00:08:00 Kurt Anderson - 15 Array Real World Examples 00:06:00 Linked Lists Kurt Anderson - 16 Nodes 00:04:00 Kurt Anderson - 16 Linked List 00:14:00 Kurt Anderson - 17 Linked List Run Times 00:15:00 Kurt Anderson - 18 Doubly Linked Lists 00:08:00 Kurt Anderson - 19 Tail Pointer 00:05:00 Kurt Anderson - 20 Linked List Real World Examples 00:03:00 Kurt Anderson - 21 Linked List Review 00:04:00 Stacks and Queues Kurt Anderson - 22 Stacks 00:10:00 Kurt Anderson - 20 Stack Example 00:11:00 Kurt Anderson - 23 Queues 00:09:00 Kurt Anderson - 24 Queue Examples 00:10:00 Kurt Anderson - 25 Queue and Stack Run Times 00:06:00 Kurt Anderson - 26 Stack and Queues Real World Examples 00:07:00 Sorting Algorithms Kurt Anderson - 27 Sorting Algorithm Introdcution 00:02:00 Kurt Anderson - 28 Bubble Sort 00:10:00 Kurt Anderson - 29 Selection Sort 00:10:00 Kurt Anderson - 30 Insertion Sort 00:09:00 Kurt Anderson - 31 Quick Sort 00:15:00 Kurt Anderson - 32 Quick Sort Run Times 00:10:00 Kurt Anderson - 33 Merge Sort 00:12:00 Kurt Anderson - 34 Merge Sort Run Times 00:08:00 Kurt Anderson - 35 Stable vs Nonstable 00:07:00 Kurt Anderson - 36 Sorting Algorithm Real World Examples 00:04:00 Trees Kurt Anderson - 37 Basics of Trees 00:08:00 Kurt Anderson - 38 Binary Search Tree 00:09:00 Kurt Anderson - 39 BST Run Times 00:08:00 Kurt Anderson - 40 Tree Traversals 00:13:00 Kurt Anderson - 41 Tree Real World Examples 00:05:00 Heaps Kurt Anderson - 42 Heap Introduction 00:04:00 Kurt Anderson - 43 Heap Step by Step 00:12:00 Kurt Anderson - 44 Heap Real World Examples 00:07:00 Conclusion Kurt Anderson - 45 Thank You 00:01:00 Course Certification Order Your Certificates and Transcripts 00:00:00

Course Overview Email marketing is the most effective and profitable marketing strategy for digital marketing. But if you don't it have proper ideas and guidelines, you might end up spending quite a large amount of money on email campaign services. Now create your own email campaigns specially designed for you and boost Your marketing even more. Learn how you can create HTML emails from scratch in this Build an HTML Email From Scratch in Just 2 hour course and stop spending money for random email campaign services. This Build an HTML Email From Scratch in Just 2 hour course will teach you how you can create HTML email services for yourself that will get you the best output. You will learn how to create HTML landing pages, HTML emails, edit email templates, algorithms, uploading images and GIF files and many other related skills that will help you boost your productivity. Learning Outcomes Learn to create HTML landing pages Be able to understand the steps of email marketing Familiarize with various HTML email templates Learn to create GIFs, Animated pictures and insert them into your email Be able to connect your Gmail account with an HTML landing page Who is this course for? This course is ideal for anyone who wants to learn how to create HTML emails and boost their email campaigning skill. You will learn the step by step process of email campaigning from this course. Entry Requirement This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Certification After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24. PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry-leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path Build an HTML Email From Scratch in Just 2 hour Course is a useful qualification to possess and would be beneficial for any related profession or industry such as: Web Developers Digital Marketers Freelancers Business Owners Programmers Unit 01: Overview of HTML Email, tools and algorithm of work Introduction 00:02:00 Tools overview 00:05:00 Algorithm 00:03:00 What is HTML Email? 00:04:00 Unit 02: Building HTML Email Building HTML Email part1 00:07:00 Building HTML Email part2 00:06:00 Building HTML Email part3 00:03:00 Building HTML Email part4 00:06:00 Building HTML Email part5 00:09:00 Building HTML Email part6 00:06:00 Tips on building Email structure part 1 00:10:00 Tips on building Email structure part 2 00:02:00 Uploading images on the internet 00:08:00 Create HTML Email template in Thunderbird email client 00:07:00 Create HTML Email template in Gmail 00:04:00 Two scenarios for your client 00:05:00 Creating GIF animated picture for Email 00:09:00 Final overview and Goodbye. 00:05:00 Resources Resources - Build an HTML Email From Scratch in Just 2 hour 00:00:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Explain the importance of BLS Aims and Objectives Explain the importance of BLS Describe the Resuscitation Council UK BLS guidelines Perform basic life support in an emergency situation Course Contents Adult basic life support algorithm Adult basic life support sequence Recovery position Resuscitation of children Resuscitation of victims of drowning Risks to the rescuer and victim Initial rescue breaths Jaw thrust Agonal gasps Use of oxygen Mouth-to-nose ventilation Bag mask ventilation Chest compressions We can also add automated external defibrillators (AED) onto the course. If you would like this added please let us know at time of booking. WHO SHOULD ATTEND? Nurses Paramedics Health Care Assistants Healthcare sector workers Anyone involved in healthcare Pharmacists

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.

Want to become an expert NLP engineer and a data scientist? Then this is the right course for you. In this course, we will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner as well.

Delve into the intricate world of 'Data Structure' with our comprehensive course, meticulously crafted for those who have a penchant for understanding the skeleton of software engineering. Data structures form the backbone of algorithmic efficiency, and mastering them is akin to holding the master key to software optimisation. Our course is a confluence of foundational knowledge and complex data structuring, ensuring that you emerge not only informed but also invigorated, ready to tackle any computational challenge thrown your way. Learning Outcomes Gain foundational understanding of different data structures and their implementations. Discover the intricate details of arrays, linked lists, stacks, and queues. Develop the ability to effectively utilise advanced structures like AVL trees and Fenwick trees. Master techniques for optimising algorithmic efficiency using suitable data structures. Enhance problem-solving skills related to data storage and retrieval. Why choose this Data Structure course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Diploma in Data Structure at QLS Level 5 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. Who is this Data Structure course for? Individuals keen on deepening their computer science foundations. Software developers aiming to optimise their code. Students pursuing computer science and related disciplines. Competitive coders desiring an edge in algorithm competitions. Tech enthusiasts eager to understand the underpinnings of efficient programming. Career path Software Developer: £25,000 - £45,000 Algorithm Engineer: £40,000 - £60,000 Data Scientist: £35,000 - £55,000 Backend Developer: £28,000 - £50,000 Systems Architect: £45,000 - £70,000 Data Engineer: £30,000 - £55,000 Prerequisites This Diploma in Data Structure at QLS Level 5 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Diploma in Data Structure at QLS Level 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. 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 £115 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 Unit 01: Introduction Module 01: Promo Video 00:02:00 Module 02: Data Structure Introduction 00:05:00 Module 03: Computational Complexity Analysis 00:13:00 Unit 02: Arrays Module 01: Static and Dynamic Arrays 00:12:00 Module 02: Dynamic Arrays Source Code 00:07:00 Unit 03: Linked List Module 01: Singly and Doubly Linked Lists 00:15:00 Module 02: Doubly Linked Lists Source Code 00:10:00 Unit 04: Stack Module 01: Stack 00:12:00 Module 02: Stack Implementation 00:04:00 Module 03: Stack Source Code 00:04:00 Unit 05: Queues Module 01: Queues (Part-1) 00:06:00 Module 02: Queues (Part-2) 00:06:00 Module 03: Queue Source Code 00:04:00 Unit 06: Priority Queues (PQs) Module 01: Priority Queues (PQs) with an interlude on heaps 00:13:00 Module 02: Turning Min PQ into Max PQ 00:06:00 Module 03: Adding Elements to Binary Heap 00:10:00 Module 04: Removing Elements from Binary Heap 00:14:00 Module 05: Priority Queue Binary Heap Source Code 00:16:00 Unit 07: Union Find Module 01: Disjoint Set 00:06:00 Module 02: Kruskal's Algorithm 00:06:00 Module 03: Union and Find Operations 00:11:00 Module 04: Path Compression Union Find 00:07:00 Module 05: Union Find Source Code 00:08:00 Unit 08: Binary Search Trees Module 01: Binary Trees and Binary Search Trees (BST) 00:13:00 Module 02: Inserting Element into a Binary Search Tree (BST) 00:06:00 Module 03: Removing Element from a Binary Search Tree (BST) 00:14:00 Module 04: Tree Traversals 00:12:00 Module 05: Binary Search Source Code 00:13:00 Unit 09: Fenwick Tree Module 01: Fenwick Tree Construction 00:06:00 Module 02: Point Updates 00:05:00 Module 03: Binary Indexed Tree 00:14:00 Module 04: Fenwick Tree Source Code 00:06:00 Unit 10: Hash Tables Module 01: Hash Table 00:17:00 Module 02: Separate Chaining 00:08:00 Module 03: Separate Chaining Source Code 00:12:00 Module 04: Open Addressing 00:11:00 Module 05: Linear Probing 00:14:00 Module 06: Quadratic Probing 00:09:00 Module 07: Double Hashing 00:15:00 Module 08: Removing Element Open Addressing 00:08:00 Module 09: Open Addressing Code 00:15:00 Unit 11: Suffix Array Module 01: Introduction 00:03:00 Module 02: The Longest Common Prefix (LCP) Array 00:03:00 Module 03: Using SA/LCP Array to Find Unique Substrings 00:05:00 Module 04: Longest Common Substring (LCS) 00:11:00 Module 05: Longest Common Substring (LCS) Full Example 00:07:00 Module 06: Longest Repeated Substring (LRS) 00:05:00 Unit 12: AVL Trees Module 01: Balanced Binary Search Trees (BBSTs) 00:09:00 Module 02: Inserting Elements into an AVL Tree 00:10:00 Module 03: Removing an AVL Tree 00:09:00 Module 04: AVL Tree Source Code 00:17:00 Unit 13: Indexed Priority Queue Module 01: Indexed Priority Queue (Part-1) 00:25:00 Module 02: Indexed Priority Queue Source Code 00:09:00 Unit 14: Sparse Tables Module 01: Sparse Table 00:26:00 Module 02: Sparse Table Source Code 00:07:00 Assignment Assignment - Diploma in Data Structure at QLS Level 5 04:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00