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Course Overview
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Artificial Intelligence and Soft Computing
- M1 : Introduction to AI & Soft Computing :- Introduction to Artificial Intelligence//
- M1 : Introduction to AI & Soft Computing :- Agent and Peas Description
- M1 : Introduction to AI & Soft Computing :- Types of Agent
- M1 : Introduction to AI & Soft Computing :- Learning Agent
- M1 : Introduction to AI & Soft Computing :- Introduction to Soft Computing//
- M1 : Introduction to AI & Soft Computing :- Soft computing vs Hard computing
- M1 : Introduction to AI & Soft Computing :- Learning and Types of Learning
- Module 2 : Solving Problem by Searching :- BFS ( Breadth First Search ) Algorithm with solved Example
- Module 2 : Solving Problem by Searching :- DFS ( Depth First Search ) Algorithm with solved Example
- Module 2 : Solving Problem by Searching :- IDFS ( Iterative Depth First Search ) Algorithm with solved Example
- Module 2 : Solving Problem by Searching :- GBFS Solved Example
- Module 2 : Solving Problem by Searching :- A Star solved Example
- Module 2 : Solving Problem by Searching :- Hill Climbing Part #1
- Module 2 : Solving Problem by Searching :- Hill Climbing Part #2 //
- Module 2 : Solving Problem by Searching :- Genetic Algorithm
- Module 2 : Solving Problem by Searching :- Genetic Algorithm Max one Problem Solved Example
- Module 2 : Solving Problem by Searching :- Min Max Solved Example
- Module 2 : Solving Problem by Searching :- Alpha-Beta Pruning Solved Example
- Module 3 – Knowledge and Reasoning :- Propositional Logic (PL) Introduction
- Module 3 – Knowledge and Reasoning :- PL to CNF conversion With Solved Example
- Module 3 – Knowledge and Reasoning :- First-Order Logic (FOL) Solved Example
- Module 3 – Knowledge and Reasoning :- Resolution Tree Sum Part #1
- Module 3 – Knowledge and Reasoning :- Resolution Tree Sum Part #2
- Module 3 – Knowledge and Reasoning :- Forward Chaining Criminal Numerical//
- Module 3 – Knowledge and Reasoning :- Backward Chaining Criminal Numerical //
- M4 : Fuzzy Set Theory & Fuzzy Rules & Inference System:- FUZZY LOGIC
- M4 : Fuzzy Set Theory & Fuzzy Rules & Inference System:- Introduction to Fuzzy Logic
- M4 : Fuzzy Set Theory & Fuzzy Rules & Inference System:- Fuzzification and De-Fuzzification
- M4 : Fuzzy Set Theory & Fuzzy Rules & Inference System:- Properties and Operation of Crisp and Fuzzy Sets
- M4 : Fuzzy Set Theory & Fuzzy Rules & Inference System:- Crisp and Fuzzy Sets and Relations
- M4 : Fuzzy Set Theory & Fuzzy Rules & Inference System:- Mamdani Fuzzy
- M4 : Fuzzy Set Theory & Fuzzy Rules & Inference System:- Fuzzy Inference System
- Module 5 : Neural Network :- Neural Network & Types //
- Module 5 : Neural Network :- Introduction to ANN and structure of ANN
- Module 5 : Neural Network :- Mc-Culloch-Pitts Neural Model
- Module 5 : Neural Network :- Neural Network Architecture
- Module 5 : Neural Network :- Perceptron Learning
- Module 5 : Neural Network :- Activation functions
- Module 5 : Neural Network :- Backpropagation Network (with solved example)
- Module 5 : Neural Network :- Self Organizing Maps and KSOMs
- Module 6: Hybrid System :- Neuro-Fuzzy System
- Module 6: Hybrid System :- Introduction to Hybrid System
- Module 6: Hybrid System :- Fuzzy Inference System
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Statistics for Machine Learning and Data Science //
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Agile Software Development and Devops //
- Module 01 – Introduction :- Software Engineering – Process Framework
- Module 01 – Introduction :- Software Development Life Cycle (SDLC)
- Module 01 – Introduction :- Software Process Models
- Module 1 – Introduction to Agile Software Development Notes
- Module 02 – Agile Processes :- Agile Manifesto and Principles
- Module 02 – Agile Processes :- Extreme Programming (XP)
- Module 02 – Agile Processes :- Agile Process Model
- Module 02 – Agile Processes :- Kanban
- Module 02 – Agile Processes :- Scrum in Agile
- Module 02 – Agile Processes Notes
- Module 02 – Agile Processes :- Test Driven Development
- Module 03 – Agile Requirements Engineering Design Notes
- Module 04 – Agile Planning & Estimation Notes
- Module 05 – Agile Quality Assurance & Testing Notes
- Module 06 – Devops :- Devops-Introduction-LMT
- Module 06 – Devops :- LMT-Devops-2
- Module 06 – Devops :- LMT-Devops-Lec-03
- Module 06 – Devops :- LMT-DEVOPS-LEC-04
- Module 06 – Devops :- LMT-DEVOPS-Lec-05
- Module 06 – Devops :- LMT-DEVOPS-LEC-06
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Computer Networks
- 1.1 Introduction to Computer Networks
- 1.2 Network Topologies
- 1.3 Network Connecting Devices
- 1.4 Connection-Oriented vs Connection-Less Communication
- 1.5 ISO-OSI Reference Model
- 1.6 TCP-IP Reference Model
- 1.7 OSI vs TCP-IP Model Comparision
- 1.8 Network Classification LAN, MAN, WAN
- 1.9 Virtual Lan’s (VLAN)
- 3.1 Framing and it’s Methods
- 3.2 Error Detection and its Methods
- 3.3 Hamming Code
- 3.4 Cyclic Redundancy Check Part [01]
- 3.4 Cyclic Redundancy Check Part [02]
- 3.6 Parity Checking and Checksum Error Detection
- 3.7 Flow Control: Stop and Wait and Sliding Window Protocol
- 3.8 Go Back- N ARQ System
- 3.9 SDLC protocol
- 3.10 HDLC protocol
- 3.11 Carrier Sense Multiple Access-Collision Detection Procedure (CSMA-CD)
- 4.1 IP address vs MAC address [Virtual Address vs Physical Address]
- 4.2 IPv4 Header Format
- 4.3 IPv4 vs IPv6 [Difference]
- 4.4 Routing Algorithms Part 01 (Static) Shortest Path Algo Dijkstra’s
- 4.5 Routing Algorithms Part 02 (Static) Flooding Shortest Path Routing
M1 : Introduction to AI & Soft Computing :- Agent and Peas Description
Agent and Peas Description
In this video we have explain what is Agent in artificial intelligence and how to write peas description for an agent. We know that there are different types of agents in AI. PEAS System is used to categorize similar agents together. The PEAS system delivers the performance measure with respect to the environment, actuators and sensors of the respective agent. PEAS stands for Performance measure, Environment, Actuator, Sensor.
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