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Statistics for Machine Learning and Data Science //
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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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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 Network [Module 1]:- Introduction to Computer Networks
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Computer Network [Module 2]:- Physical and Data Link layer
- Transmission Media: Guided and Unguided [Difference]
- Twisted-Pair Cables
- CoAxial Cable
- Framing and it’s Methods
- Fiber Optics Part [01]
- Fiber Optics Part [02]
- Error Detection and its Methods
- Hamming Code
- Cyclic Redundancy Check Part [01]
- Cyclic Redundancy Check Part [02]
- Parity Checking and Checksum Error Detection
- Flow Control: Stop and Wait and Sliding Window Protocol
- Go Back- N ARQ System
- SDLC protocol
- HDLC protocol
- Carrier Sense Multiple Access-Collision Detection Procedure (CSMA-CD)
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Computer Network [Module 3]:- Network Layer
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Computer Network [Module 4]:- Transport and Application Layer
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Computer Network [Module 5]:- Enterprise Network Design
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Computer Network [Module 6]:- Software Defined Networks
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Computer Network [Notes]:- Data Warehousing Fundamentals
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Computer Network [Notes]:- Physical Layer
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Computer Network [Notes]:- Data Link Layer
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Computer Network [Notes]:- Network Layer
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Computer Network [Notes]:- Transport Layer
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Computer Network [Notes]:- Application Layer
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Computer Network [ Importance ] :-
- IPv4 & IPv6 [ Numerical Notes ]
- Cyclic Redundancy Sums [ Numerical Notes ]
- Error Hamming [ Numerical Notes ]
- Introduction To Networking
- Physical Layer & Data Link Layer
- Network Layer
- Transport Layer & Application Layer
- Enterprise Network Design
- Software Defined Networks
- [Extra] Previously Asked Important Questions
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Computer Network [Viva Questions]:-
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Data Warehouse and Data Mining [Module 1]:- Data Warehousing Fundamentals
- Introduction to Data Warehouse
- Meta Data
- Data Mart
- Data Warehouse Architecture
- How to Draw Star , Smowflake and Fack Constelation Basics
- Numericals on Star , Snowflake and Fact Constelation [ Part 1 ]
- Numericals on Star , Snowflake and Fact Constelation [ Part 2 ]
- What is Olap Operations
- OLAP VS OLTP
- Extract Transform and Load (ETL)
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Data Warehouse and Data Mining [Module 2]:- Introduction to Data Mining, Data Exploration
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Data Warehouse and Data Mining [Module 3]:- Classification
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Data Warehouse and Data Mining [Module 4]:- Clustering
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Data Warehouse and Data Mining [Module 5]:-Mining frequent patterns and associations
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Data Warehouse and Data Mining [Module 6]:- Web Mining
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Data Warehouse and Data Mining :- Notes + Importance Solution
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Module 2 : Solving Problem by Searching :- Genetic Algorithm Max one Problem Solved Example
Genetic Algorithm Max one Problem Solved Example
Max One Problem is the simplest problem that only performs the calculation of the maximum value from a number of binary strings. Usually, Max One Problem is used to represent an algorithm i.e. Genetic Algorithm. Later, the result will be compared with conventional genetic algorithms. In this video we have taken an example of a Genetic Algorithm and explain the different steps involved in Genetic algorithm
Five phases are considered in a genetic algorithm.
- Initial population
- Fitness function
- Selection
- Crossover
- Mutation
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