3807 Engineer courses in Macclesfield delivered Online

Session Border Controllers course description A hands on course covering Session Border Controllers with a focus on the technical workings of features commonly found in Session Border Controllers. Hands on practicals follow each major theory session. What will you learn Explain how Session Border Controllers work. Explain the SIP call flow using a SBC. Deploy Session Border Controllers Describe the features found in Session Border Controllers. Session Border Controllers course details Who will benefit: Technical staff working with SIP. Prerequisites: Definitive SIP for engineers Duration 2 days Session Border Controllers course contents SIP review Signalling, media, RTP, SIP, peer to peer, SIP proxies, SIP call flows. Hands on: SIP packet analysis. Session Border Controllers What is a SBC? SBC features, peering scenario, access scenario, NNI SBC, UNI SBC, enterprise SBC. Hands on: SBC call flows SBC topology hiding Header privacy, B2BUA. Hands on: Header analysis. Session control Call admissions, QoS, statistics and billing, redundancy and scalability. Hands on: SBC session control SBC and NAT NAT traversal, STUN, ICE, NAT and SIP, NAT and RTP. Hands on: SBC and NAT traversal. SBC and security DoS, access control, encryption, authentication, toll fraud, regulatory issues, lawful intercept. Hands on: Security. Interoperability mediation Manipulating SIP headers, IMS, IETF, TISPAN, SIP-I. SBC interworking.

Zeroconf and Bonjour training course description A hands on training course focusing on Microsoft and Apple implementations of Zeroconf. The course covers all three main areas: Interface configuration, name resolution and service discovery. Hands on with Apple Bonjour and Microsoft UPnP compliment all the major theory sessions. What will you learn Explain how mDNS and LLMNR work. Explain how DNS-SD and SSDP work. Recognise the role of service discovery gateways. Zeroconf and Bonjour training course details Who will benefit: Technical staff working with Zeroconf. Developers using Zeroconf. Prerequisites: TCP/IP Foundation for engineers Duration 2 day Zeroconf and Bonjour training course contents What is Zeroconf? Zeroconf, architecture, Microsoft UPnP, Apple, Bonjour, devices, components. Home networks, enterprise networks, BYOD. Plug and play without Zeroconf DHCP, DNS, A, PTR, SRV records, DDNS. Hands on DHCP configuration. Address selection IPv4 link local addresses, IPv6 link local addresses. Hands: Addresses without DHCP. Name resolution mDNS, finding names, announcing names, .local DNS namespace, LLMNR. Hands on Names without DNS. Role of multicasting Multicast addresses, multicasts and switches, multicasts and routers. Hands on Multicasts, TTL. Service discovery DNS-SD, SRV and TXT lookups, SSDP, HTTP. Hands on Browsing for services Zeroconf in a routed environment Service Discovery gateways, configuration, service filters, DNS-LLQ, NAT-PMP. Miscellaneous Security, automatic multicast addresses, wireless auto configuration.

Advanced HP switches training course description A hands on course covering advanced features of HP switches. The focus of the course is on Layer 3 switching advanced features. Delegates will benefit the most from this course if they can supply us with their own configurations before the course. These configurations can then be used by the trainer in the course. What will you learn Configure and troubleshoot HP layer 3 switching. Configure and troubleshoot OSPF Harden HP switches. Explain the majority of their own configurations (if configurations are supplied to us before the course). Advanced HP switches training course details Who will benefit: Technical staff working with HP switches. Prerequisites: HP switches for engineers. Duration 2 days Advanced HP switches training course content Introduction VLAN review. Tagged and untagged ports. MSTP, PVST, UDLD, BPDU guard. Hands on VLANs, diagnostic tools debug, LLDP, remote port mirroring. L3 switching What are L3 switches? L3 lite, IPv4 and IPv6, routing protocols, routing tables. IP address on VLAN, no ip address on VLAN, connected interfaces, RIP. Hands on Routing between VLANs. Configuring and troubleshooting RIP. VRRP Default gateways, dead router detection, resilience. Hands on Configuring and troubleshooting VRRP. Static routes Why use static routes? Configuring static routes, default routes. OSPF Link state protocols, operation. Router types, LSA types, area 0. Hands on Configuring and troubleshooting OSPF. QoS Layer 2 QoS, 802.1Q, priorities, dscp mapping, rate limiting. Access lists Filtering traffic through the switch, controlling access to the switch, enabling and disabling ACLs, standard ACLs, extended ACLs Security DHCP snooping, ARP protection, STP guards, switch hardening. Summary Walkthrough of delegate configurations.

Broadband access training course description ADSL is a broadband technology providing fast Internet access (amongst other applications) over existing telephone lines. This course covers an overview of the DSL family, what ADSL is through to how ADSL works. What will you learn Describe what ADSL is. Describe how ADSL works. Describe the ADSL architecture. Recognise the limitations of ADSL. List the elements required for an ADSL installation. Broadband access training course details Who will benefit: Network engineers and anyone who will be working with ADSL. Prerequisites: Intro to data communications & networking Duration 2 days Broadband access training course contents What is ADSL? Broadband definitions, OSI layer 1, ADSL services, WANS. ADSL features: always on, point to point, Asymmetric, speeds. ADSL benefits, xDSL family, standards, history, example DSL forum documents. ADSL architecture The big picture, The PSTN and telephones, Digital and analogue, PSTN and modems, ADSL vs. modem speeds, Block 1: Customer premises, Block 2: The last mile, Block 3: The exchange, Block 4: the core network. Customer premises Splitters, micro filters, splitter architectures, Splitterless ADSL, ADSL modems, USB, ADSL routers. The local loop ADSL PHY, Some basics, ADSL margins, speed implications, distances, RADSL, Line testing, whoosh tests, line coding, multiple channels, FDM, echo cancellation, Modulation: AM, FM, PM, QAM, QAM constellations, DMT, CAP, Framing, Superframes, fast data mode, interleaved mode, RADSL revisited. The exchange Local exchange ADSL items, DSLAMs, ADSL racks, Contention. The core network The role of the core network, ATM, ATM VPI/VCI, ATM cells, ATM layers, AAL5, RAS, Home gateways. ADSL and the higher layers Layer 2 choices, PPPoA, PPP, CHAP, Layer 4 and above, ADSL and ATM. Installing and configuring ADSL Choosing providers, line activation, hardware requirements, Configuring layer 1 and layer 2, Configuring IP. Summary ITU ADSL standards

Gain n-depth knowledge about Docker technology and the confidence to help your company or your own project to apply the right Docker deployment workflow. Learn all about Docker, Docker REST API, and Docker continuous integration to build Docker images.

Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Apache training course description A hands on training course covering installation, configuration and management of the Apache web server. What will you learn Install Apache. Configure Apache. Manage Apache. Build static and dynamic web sites with Apache. Secure Apache. Apache training course details Who will benefit: Technical staff working with Apache. Prerequisites: TCP/IP foundation for engineers. UNIX fundamentals Duration 3 days Apache training course contents Installing Apache What is Apache? Apache versions, history, downloading Apache, source distribution, compilation, binary distribution, installation, platform considerations. Hands on Downloading and installing Apache. Controlling the Apache server Running Apache, automatic Apache start, starting, stopping, restarting Apache. Checking Apache status. Hands on Server control. Configuration Serving webpages, setting the document root, applying configuration changes, Configuration files, httpd.conf, syntax, directives, modules, utilities, turning features on/off. Hands on basic Apache configuration. More configuration MIME, URL mapping, content negotiation, indexing, performance tuning. Logging log file content, configuration, log file locations, error logging, browser errors, error page configuration, forbidden index pages. Hands on Log files. Security File permissions, .htaccess, protecting files with passwords, password files, authentication, restricting access by IP address. Secure HTTP HTTPS, installing mod_ssl, certificates, configuring mod_ssl, http and https coexistence Virtual hosts Multiple sites on one server, separate configuration files, IP based, name based, port based, virtual host names, enabling, defining, configuring, aliases, testing, https virtual hosts. Hands on Virtual hosts. Dynamic sites Dynamic sites, CGI, PHP, PERL, CGI programs, example CGI scripts, Apache and CGI, CGI parameters, CGI issues, PHP, mod_php, Perl and Apache, mod_perl, installing mod_perl. Hands on CGI, PHP and Perl with Apache. Modules What are modules, standard modules, loading modules, mod_speling, mod_rewrite, other special purpose modules, URL rewriting, redirection, URL transformation, browser dependent pages. Hands on Working with modules.

IPv6 training course description IPv6 is the next generation Internet Protocol. This hands-on course looks at the benefits and features of the new protocol along with an assessment of the likely impact of the protocol and migration strategies. Practical exercises using PCs and routers follow the major sessions in order to reinforce the theory. What will you learn Configure PCs and routers for IPv6. Troubleshoot IPv6 networks. Analyse IPv6 packets. Plan migration strategies for IPv6. Integrate IPv6 and IPv4 networks. IPv6 training course details Who will benefit: Anyone working in the field of networking. Prerequisites: TCP/IP Foundation for engineers Duration 3 days IPv6 training course contents Introduction Reasons for IPv6, IPv4 weaknesses, what is IPv6? IPv4 solutions for solving address wastage, the origins of IPv6. hands on IPv6 on a PC, IPv6 on a router. IPv6 addressing IPv6 address allocation, address format, Prefixes but no masks, address categories, scope zones, aggregatable global unicast, link local, Unicast, Multicast, Anycast. Prefix delegation. hands on Link local addresses, manual address configuration, name resolution. Plug and play Plug and play addressing, ICMP neighbour discovery, router solicitation, DHCPv6, stateful autoconfiguration and stateless autoconfiguration. hands on Plug and play addresses and default gateways. The IPv6 header The IPv4 header, IPv6 header format, QoS, flow control, priority field, extension headers, hop by hop, destinations header, fragmentation header, security, IPsec, AH, ESP, TCP and UDP, ICMPv6. hands on IPv6 packet analysis. Migrating to IPv6 Overview, migration, dual stack, IPv4 compatible addresses, DNS, IPv6 DNS issues, AAAA records, IPv6 reverse delegation, DNS transport, protocol translators, NAT-PT, NAPT-PT, NAT64, DNS64, tunnelling, tunnel establishment, tunnel brokers, Tunnel types. hands on Dual stack operation, tunnelling, IPv6 name resolution. IPv6 routing IPv6 routing, RIPng packet format, RIPng for IPv6, OSPF for IPv6, MBGP, multiprotocol routing, MBGP and multicasts, MBGP and IPv6. hands on Base router setup for IPv6, IPv6 static routes, RIPng, OSPFv3. MBGP

SIP training course description A hands on course covering IP telephony with SIP. The course starts with a brief review of knowledge students should already possess including RTP and RTCP. The main focus is on SIP though, progressing from what SIP is through SIP signalling, call processing and architectures, moving onto more advanced issues including security, multimedia, and interoperability. Hands-on practicals follow each major theory session. What will you learn Explain how SIP works. Analyse SIP packets. Deploy SIP IP telephony solutions. Integrate SIP with other telephony solutions. SIP training course details Who will benefit: Technical staff working with SIP. Prerequisites: Definitive VoIP for engineers Duration 3 days SIP training course contents VoIP review What is VoIP? Brief review of IP, Brief review of telephones and voice. RTP, RTCP, mixers and translators. Hands on Analysing RTP packets. What is SIP? Why SIP? SIP history, SIP standards, SIP capabilities, key services, how SIP works, and a basic SIP call. Hands on Peer to peer SIP. SIP messages SIP sessions, SIP flows, Message structure, INVITE, ACK, BYE, CANCEL, OPTIONS, REGISTER. Extension methods. Response codes. SIP call flows. Hands on Analysing SIP packets. SIP architectures UA client, UA server, Proxy servers, Redirect servers, registrars. SIP phones, gateways, application servers, and other products. Stateful and stateless servers. Various call scenarios. Hands on SIP proxies. SIP addressing URLs, SIP addresses, registration, Location and Directory servers. Address tracking. Hands on SIP and DNS. Supplementary services SIP signalling, signalling compression, Call hold, Call forwarding, Home and away scenarios, transfers, conferences, call control. Hands on Analysing SIP supplementary services. SDP What is SDP? Multimedia, multimedia session announcement, invitation and others. Relationship with SIP. Hands on Video conferencing with SIP. SIP security Access control, Authentication, encryption, firewalls. Hands on SIP authentication. Interoperability Inter working with PSTN, ISUP to SIP mapping, SIP and 3G, SIP-T, SIP and SIGTRAN. SIP and H323. Hands on SIP and gateways. SIP mobility Terminal mobility, service mobility, personal mobility, Mobile IP, SIP signalling flows in 3G.