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About the Course
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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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