Array, Linked List, Queue, Stack, Tree, Graph

• Discussed property of Array (e.g., array indexing, editing, adding elements). Introduced Linked List as connected data structure.
• Discussed usefulness of stack and queue for search algorithms in Tree and Graph.
• Explored Tree data structure with application.
• Explored Graph data structure with application.

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AI Search Algorithms, Tree and Graph Algorithms

• Demonstrated usefulness of Minimal Spanning Tree in Tree Data structure.
• Implemented BFS and DFS search algorithms in both Tree and Graph Data Structure.
• Implemented advanced algorithms in real case scenario : smart Drone network in territory of Pokhara.

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CNN, RNN, GAN

• Explored Convolutional Neural Network and trained the model with image data.
• Implemented Feature Scaling method and Call back techniques for preprocessing and intermediate model configuration.
• Practised Model tuning with hyper parameters, epoch, number of neurons and batch sizes.

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MongoDB, Map Reduce, Spark, Neo4J, Elastic Search

• Introduced MongoDB as NoSQL database and explored MongoDB Atlas with python API for data analytics platform.
• Explored the map reduce paradigm of distributed computing with databrick's Spark platform (e.g., spark RDD, delta lake, MLflow)
• Implemented neopy to create an API to connect graphical database with Neo4j.
• Performed graphical data exploration with Neo4J graph and Network (e.g., DFS, BFS, Centrality, Path).
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Quantum Gates, Circuits, Quantum Algorithms

• Introduced Quantum Gates in Python with QisKit package (e.g., Hadamard gates, Pauli gates, swap gate, not gate ).
• Explored single qubit and multi qubit quantum circuits.
• Explored random quantum circuit with fusion of different quantum gates.
• Explored Bell state and Teleportation with combination of quantum gates.
• Performed simulation of quantum algorithms (e.g., Quantum Fourier Transform, Grover's Algorithm).

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