Hello everyone. Stay tuned for content related to computing technology. I will be coming up with tutorials, research talks, discussions, innovations, and a lot more.

How to Identify a Perfect Research Problem Domain (With Real Example)
5:55
How to Identify a Perfect Research Problem Domain (With Real Example)
Introduction to Academic Research
4:34
Introduction to Academic Research
Day 29: Model Deployment & Integration | Keras, PyTorch, Sklearn + GUI App
6:34
Day 29: Model Deployment & Integration | Keras, PyTorch, Sklearn + GUI App
Research Talk 001: Data Augmentation for Brain Tumor Classification in MRI (ICETECC 25)
12:08
Research Talk 001: Data Augmentation for Brain Tumor Classification in MRI (ICETECC 25)
Genetic Algorithm Explained with Examples | Maximize Function & Solve N-Queens Problem
7:09
Genetic Algorithm Explained with Examples | Maximize Function & Solve N-Queens Problem
Genetic Algorithms Explained |Representation|Fitness Evaluation|Parent Selection| Crossover Mutation
10:45
Genetic Algorithms Explained |Representation|Fitness Evaluation|Parent Selection| Crossover Mutation
Local Search Algorithms Explained | Hill Climbing, Stochastic, First-Choice, Random Restart Examples
8:32
Local Search Algorithms Explained | Hill Climbing, Stochastic, First-Choice, Random Restart Examples
Alpha-Beta Pruning Explained with Easy Examples
8:43
Alpha-Beta Pruning Explained with Easy Examples
Adversarial Search in AI: Minmax Algorithm Explained!
8:21
Adversarial Search in AI: Minmax Algorithm Explained!
A Star Search Algorithm Explained with Water Jug & Pac-Man Game Examples
10:18
A Star Search Algorithm Explained with Water Jug & Pac-Man Game Examples
Understanding A* Search and Admissibility
9:53
Understanding A* Search and Admissibility
Greedy Best-First Search Explained | Informed Search & Heuristics
6:47
Greedy Best-First Search Explained | Informed Search & Heuristics
BFS and DFS Graph Traversal Algorithms Explained
7:45
BFS and DFS Graph Traversal Algorithms Explained
Uniform Cost Search (UCS) Explained: Step-by-Step Example & Comparison
6:44
Uniform Cost Search (UCS) Explained: Step-by-Step Example & Comparison
Iterative Deepening Search & Depth-Limited Search Explained with Examples
5:39
Iterative Deepening Search & Depth-Limited Search Explained with Examples
Graph Search Algorithms: Solving a Maze with BFS and DFS
5:03
Graph Search Algorithms: Solving a Maze with BFS and DFS
AI Search Algorithms
4:52
AI Search Algorithms
Day 28: Evaluation Metrics for Clustering
7:00
Day 28: Evaluation Metrics for Clustering
Day 27: Performance Evaluation Metrics for Classification and Regression
7:25
Day 27: Performance Evaluation Metrics for Classification and Regression
Day 26: Agglomerative Clustering | Hierarchical Clustering
8:00
Day 26: Agglomerative Clustering | Hierarchical Clustering
Day 25: Clutering Algorithms | K-Means | K-Medoids
9:56
Day 25: Clutering Algorithms | K-Means | K-Medoids
Day 24: Classification Algorithms | Logistic Regression| kNN
7:52
Day 24: Classification Algorithms | Logistic Regression| kNN
Day 23: Simple and Multiple Regression | Numerical and Code example
10:52
Day 23: Simple and Multiple Regression | Numerical and Code example
Day 22: Introduction to Machine Learning Modeling
6:40
Day 22: Introduction to Machine Learning Modeling
Day 21: Data Augmentation Techniques | SMOTE Implementation in Python
7:05
Day 21: Data Augmentation Techniques | SMOTE Implementation in Python
Day 20: Feature Selection Techniques | Information Gain | Forward Selection| Backward Elimination
10:58
Day 20: Feature Selection Techniques | Information Gain | Forward Selection| Backward Elimination
Performance Evaluation Metrics | Classsification model | Confusion Matrix
19:02
Performance Evaluation Metrics | Classsification model | Confusion Matrix
Day 19: Principal Component Analysis (PCA) | Dimensionality Reduction
9:53
Day 19: Principal Component Analysis (PCA) | Dimensionality Reduction
What is Multimodal AI?
4:12
What is Multimodal AI?
K-Nearest Neighbors (KNN) - Supervised Machine Learning
13:07
K-Nearest Neighbors (KNN) - Supervised Machine Learning