Sem 6 AIML
₹299.00
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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
Sem 6 AIML / AIDS Bundle [ ML – DVA – CSS – SEPM – DC ]
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Course Features
- Lectures 99
- Quizzes 0
- Duration 10 hours
- Skill level All levels
- Language Hindi
- Students 11
- Certificate No
- Assessments Yes
