Courses

Introduction to wired networking fundamentals, including Ethernet and cable types (straight-through and cross-wired).

Configuration and troubleshooting of LAN topologies using Cisco Packet Tracer simulations.

Detailed study of TCP/IP protocols, including IP addressing, subnetting, and routing.

Implementation of VLANs and static/dynamic routing protocols such as RIP, OSPF, and BGP.

Packet capture and analysis using Wireshark for TCP, UDP, and IP headers.

Understanding data link layer services like error detection (CRC, checksum) and multiple access protocols.

Exploration of switching techniques, bridging concepts, and Ethernet standards.

Design and implementation of secure network architectures with firewalls and access control lists (ACLs).

Study of network performance metrics like latency, throughput, and QoS in wired setups.

Hands-on labs for real-world network troubleshooting using simulation tools.

Overview of wireless networks, including 2G, 3G, and 4G technologies with global industry insights.

Introduction to 5G evolution, service verticals, use cases, and network architecture.

Detailed study of 5G network interfaces and protocols: Air, Xn, F1, NG-C, NG-U, SBA.

Exploration of 5G NR protocol stack layers (RRC, SDAP, PDCP, MAC, RLC, PHY) and physical layer procedures like MIMO and beamforming.

Simulation of PHY layer concepts using tools like MATLAB/GNU Octave for waveform analysis and numerologies.

Analysis of 5G core network protocols and call flows using Wireshark: Registration, PDU session establishment, and QoS management.

Hands-on experimentation with open-source 5G Core and RAN frameworks for network deployment strategies (SA & NSA).

Study of Telco Cloud enablers like virtualization, containerization (Docker/Kubernetes), SDN/NFV, MEC, and network slicing.

Live demonstrations of deploying 5G Core on Telco Cloud platforms with real-world scenarios.

Exploration of Beyond 5G technologies and open research areas toward 6G innovations.

Introduction to AI, ML, and DL concepts, including historical development and key applications.

Supervised, unsupervised, and reinforcement learning paradigms with hands-on implementation using Google Colab and Kaggle.

Exploration of ML algorithms such as linear regression, decision trees, k-nearest neighbors, and clustering techniques.

Neural network fundamentals: architecture, activation functions, forward/backward propagation, and optimization techniques.

Deep learning frameworks (TensorFlow, Keras, PyTorch) for building CNNs, RNNs, and autoencoders.

Practical NLP applications: sentiment analysis, text classification, and sequence-to-sequence models using Jupyter Notebooks.

Experimentation with transfer learning techniques for image classification and object detection tasks.

Data preprocessing, feature engineering, and model evaluation metrics for real-world datasets using Anaconda.

Advanced DL concepts like GANs (Generative Adversarial Networks) and reinforcement learning models in VS Code.

Capstone projects integrating AI/ML/DL techniques for solving complex industry problems.