A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2025 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
ROADMAP(Mind Map) and KEYWORD for students those who have interest in learning NLP
collections of data science, machine learning and data visualization projects with pandas, sklearn, matplotlib, tensorflow2, Keras, various ML algorithms like random forest classifier, boosting, etc
List of DL topics and resources essential for ing interviews
distfit is a python library for probability density fitting.
Second edition of Springer Book Python for Probability, Statistics, and Machine Learning
Random vectors: marginal and conditional distributions. Normal, t-distribution, Chi-square and F-distribution... AND A LOT MORE.
Collection of all courses, and their materials, attended at Politecnico di Milano during both Bachelor level degree and Master level degree in Engineering, Computer Science Engineering
This repository includes academic notes, study materials, and resources from B.Tech (Hons) in CSE, specializing in Artificial Intelligence and Data Science. It features question papers, proprietary study guides, and resources to support learning in these fields.
Machine learning resources (Jupyter notebooks mostly). Originally code to complement the "EECE 5644: Introduction to Machine Learning and Pattern Recognition" course taught at Northeastern University.
pg_math extension to support statistical distribution functions for PostgreSQL
A math resource for CS student
A curated list of references to help you get up to speed with the concepts and techniques needed to become a successful ML researcher.
Computer Science PDF notes
All the homeworks, testers and projects done at Marmara University, Computer Science & Engineering
Subset simulation is a method of estimating low probability events. Here I adapt SS to perform well with correlated inputs.
Projects of a CSE student at Marmara University
Trimmed L-moments and L-comoments for robust statistics.
Interactive courseware module that addresses common foundational-level concepts taught in statistics courses.
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