Networking Courses Online
Networking Live Instructor Led Online Training Networking courses is delivered using an interactive remote desktop.
During the Networking courses each participant will be able to perform Networking exercises on their remote desktop provided by Qwikcourse.

Networking Fundamentals
About
Networking is a group of computers that use a set of common communication protocols over digital interconnections for the purpose of sharing resources located on or provided by the network nodes. The interconnections between nodes are formed from a broad spectrum of telecommunication network technologies, based on physically wired, optical, and wireless radio-frequency methods that may be arranged in a variety of network topologies.
The nodes of a computer network may include personal computers, servers, networking hardware, or other specialized or general-purpose hosts. They are identified by hostnames and network addresses. Hostnames serve as memorable labels for the nodes, rarely changed after the initial assignment. Network addresses serve for locating and identifying the nodes by communication protocols such as the Internet Protocol.
Computer networks may be classified by many criteria, for example, the transmission medium used to carry signals, bandwidth, communications protocols to organize network traffic, the network size, the topology, traffic control mechanism, and organizational intent.
Computer networks support many applications and services, such as access to the World Wide Web, digital video, digital audio, shared use of application and storage servers, printers, and fax machines, and use of email and instant messaging applications.
Content
- Overview
- Network packet
- Network topology
- Overlay network
- Network links
- Wired
- Wireless
- Network nodes
- Network interfaces
- Repeaters and hubs
- Bridges and switches
- Routers
- Modems
- Firewalls
- Communication protocols
- Common protocols
- Internet Protocol Suite
- IEEE 802
- Ethernet
- Wireless LAN
- SONET/SDH
- Asynchronous Transfer Mode
- Cellular standards
- Routing
- Common protocols
- Geographic scale
- Organizational scope
- Intranet
- Extranet
- Internet
- Darknet
- Network service
- Network performance
- Bandwidth
- Network delay
- Quality of service
- Network congestion
- Network resilience
- Security
- Network security
- Network surveillance
- End to end encryption
- SSL/TLS
- Views of networks
OpenFlow Fundamentals
About
OpenFlow is a computer network protocol for software-defined networking.
Content
- OpenFlow communication channel
- Manage multiple switches
- Standard OpenFlow controller and switch procedures
- Tables in OpenFlow switches
- Controller and switch procedures
Learn Zabbix for Network Monitoring
About
Zabbix is an open-source monitoring software tool for diverse IT components, including networks, servers, virtual machines (VMs), and cloud services. Zabbix provides monitoring metrics, among others network utilization, CPU load, and disk space consumption. Zabbix monitoring configuration can be done using XML-based templates that contain elements to monitor. The software monitors operations on Linux, Hewlett Packard Unix (HP-UX), Mac OS X, Solaris, and other operating systems (OSes); however, Windows monitoring is only possible through agents. Zabbix can use MySQL, MariaDB, PostgreSQL, SQLite, Oracle, or IBM DB2 to store data. Its backend is written in C and the web frontend is written in PHP. Zabbix offers several monitoring options:
- Simple checks can verify the availability and responsiveness of standard services such as SMTP or HTTP without installing any software on the monitored host.
- A Zabbix agent can also be installed on UNIX and Windows hosts to monitor statistics such as CPU load, network utilization, disk space, etc.
- As an alternative to installing an agent on hosts, Zabbix includes support for monitoring via SNMP, TCP, and ICMP checks, as well as over IPMI, JMX, SSH, Telnet, and using custom parameters. Zabbix supports a variety of near-real-time notification mechanisms, including XMPP.
Content
- Features
- Installation and Configuration
- Development
- Source code
Twisted
About
Twisted is an event-driven network programming framework written in Python and licensed under the MIT License.
Twisted projects variously support TCP, UDP, SSL/TLS, IP multicast, Unix domain sockets, many protocols (including HTTP, XMPP, NNTP, IMAP, SSH, IRC, FTP, and others), and much more. Twisted is based on the event-driven programming paradigm, which means that users of Twisted write short callbacks which are called by the framework.
Content
- Core ideas
- Separation of protocols and transports
- Deferreds
- Thread support
- Foreign loop support
- Applications using Twisted
Learn Network Analysis Using Wireshark
About
Wireshark is Network traffic analyzer It is a packet analyzer. Wireshark is developed by The Wireshark teamSupported by GNU/Linux, BSD and Microsoft Windows Operating Systems.
Content
ANTFARM
ANTFARM is a passive network mapping application that utilizes output from existing network examination tools to populate its OSI-modeled database. This data can then be used to form a ‘picture’ of the network being analyzed. ANTFARM is a data fusion tool that does not directly interact with the network. The analyst can use a variety of passive or active data gathering techniques, the outputs of which are loaded into ANTFARM and incorporated into the network map. Data gathering can be limited to completely passive techniques when minimizing the risk of disrupting the operational network is a concern. Code development takes place from GitHub. The latest source code for each ANTFARM release is mirrored at the RubyForge project as well as SCM.
VisualizeMNIST
VisualizeMnist
This course is real-time visualization of a network recognizing digits from user's input. I trained a network using MNIST dataset and parsed the weight data in python. With this data, I implemented my own custom functions that are needed to run the network in Processing including matrix multiplication function, activation functions. At first trial, because MNIST dataset is preprocessed for numbers to be in the center of the images, there was a precision problem when the user's input is placed little bit far away from the center. I used data augmentation technic in the training process to resolve this problem.
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