Mathematics behind ML algorithms Archives

Know Normal Distribution and Z-score as a Data Scientist

This blog post provides a comprehensive guide to understanding normal distributions, often referred to as Gaussian distributions. It covers foundational concepts such as discrete and continuous variables, probability distribution, expectation, variance, standard deviation, the normal distribution equation, the 68-95-99 rule, and z-scores. The post employs practical examples and Python code to illustrate these mathematical concepts, pertinent to both engineering and machine learning contexts.

Posted on June 12, 2023 Tagged with Machine Learning, Mathematics behind ML algorithms, normal distribution, statistics, z score

ROC curve and AUR in the simplest way

How would you know if your model is good enough? ROC curve help us select the right threshold and can compare two or more models together.

Posted on May 2, 2023 Tagged with cross validation, Machine Learning, Mathematics behind ML algorithms, ROC curves

BFS and DFS Algorithms For Visual Learners

Artificial Intelligence harnesses algorithms like Breadth First Search (BFS) and Depth First Search (DFS) for effective problem-solving. Before delving into these techniques, it’s vital to understand the core of a search algorithm. From ancient foragers to modern navigators, our pursuit of solutions has evolved. Illustrated through Lisa’s quest for a rare lipstick shade, BFS shows organized yet potentially redundant traits, while the strategic DFS proves more efficient. Both algorithms face challenges in larger search spaces, paving the way for future exploration into heuristic solutions. Connect for further insights.

Posted on April 25, 2023 Tagged with Breadth First Search, Depth First Search, Machine Learning, Mathematics behind ML algorithms, mini blog

Crazy Mathematics behind SVM in a Simple way

SVM, a potent algorithm championed by Vladimir N. Vapnik, triumphed in image classification after being overlooked for three decades. This supervised machine learning tool classifies data points with hyperplanes, excelling in both binary and multilinear classification. SVM’s quest for an optimal hyperplane involves maximizing margin, achieved through Lagrange Multipliers and the Kernel Trick. Though not the primary choice for modern image classification, SVM proves effective for datasets with fewer parameters, showcasing that machine learning at its core is deeply intertwined with mathematics.

Posted on December 25, 2022 Tagged with Machine Learning, Mathematics behind ML algorithms, Support Vector Machine

Word2Vec and it’s smartest overview

This blog offers a hands-on exploration of Word2Vec, unraveling its purpose, functionality, and practical implementation. Vital in Natural Language Processing, Word2Vec excels in contextual word vectorization. Unlike simplistic approaches, it positions words with akin meanings closer in vector space. The blog provides a succinct yet comprehensive overview, introducing the Skip-gram model and culminating in a simplified Python-based implementation using Gensim. Essential for those seeking a swift entry into impactful word representation.

Posted on October 21, 2022 Tagged with Machine Learning, Mathematics behind ML algorithms, natural language processing, word2vec

Precision, Recall and F1 Score For Data Scientists

Why do we need precision and recall? How do we get f1 score? what does f1 score represent? We will answer all of these questions.

Posted on June 14, 2022 Tagged with Machine Learning, Mathematics behind ML algorithms, precision and recall, validation metrics

Tag: Mathematics behind ML algorithms

Know Normal Distribution and Z-score as a Data Scientist

ROC curve and AUR in the simplest way

BFS and DFS Algorithms For Visual Learners

Crazy Mathematics behind SVM in a Simple way

Word2Vec and it’s smartest overview

Precision, Recall and F1 Score For Data Scientists