Blog's Topic:

Data Science

Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain expertise to extract knowledge and insights from data. It involves collecting, cleaning, and processing large datasets to uncover hidden patterns, trends, and valuable information.
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Machine Learning Optimization with Gradient Descent
·3 min read
Gradient Descent is optimization algorithm of find optimal solution to problems. In Machine Learning it is used to find optimal parameters of model to minimize cost function.Evaluate Machine Learning Performance
·4 min read
Evaluating Machine Learning performance is essential for selecting right model, tuning parameters, detecting overfitting/underfitting, and assesing the model generalization ability to new data (unseen data in training process).Machine Learning: Perceptron Classifier
·3 min read
Perceptron Classifier is one of the classification algorithms in machine learning. It is similar to linear regression model because it uses combination of features to predict.Machine Learning: Underfitting, Overfitting, and Generalization
·3 min read
Machine learning models aim for generalization to new data. Underfitting (too simple) and overfitting (too complex) hinder this. Achieving balance ensures effective, robust performance.Machine Learning: K-Means Clustering
·2 min read
K-Means is a popular clustering algorithm, simplifying data partitioning based on similarity for efficient pattern recognition in diverse applications.Machine Learning: Fundamental Concepts and Algorithms
·2 min read
The concepts of linear regression, classification, clustering, and Hidden Markov Model (HMM) represent different types of machine learning algorithms or models.Machine Learning: What are Tensors?
·2 min read
In machine learning, tensors are crucial multi-dimensional arrays for data representation and manipulation in diverse algorithms.Paradigms of Machine Learning
·7 min read
Machine learning encompasses supervised, unsupervised, reinforcement, semi-supervised, and self-supervised learning paradigms, each tailored for specific tasks and objectives.Exploring Search Algorithms: Uninformed and Informed Search
·3 min read
What is different uninformed and informed search for problem solving?Exploring Search Algorithms: Breadth First Search (BFS) VS Depth First Search (DFS)
·3 min read
Different between Breadth First Search (BFS) and Depth First Search (DFS).