A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2026 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

Probability and Statistics for Data Science: A self-contained introduction to probability and statistics for data science, including a free pdf, 103 Python notebooks using 23 real-world datasets, 118 videos with slides, and solutions to 200 exercises

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 cracking 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.

Computer Science PDF notes

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

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.

A math resource for CS student (I have decided to refactor the contents to my personal blog and continue working on this, so the project is archived)

Statistical Distribution Functions(GNU scientific library) Extension for PostgreSQL

A curated list of references to help you get up to speed with the concepts and techniques needed to become a successful ML researcher.

All the homeworks, testers and projects done at Marmara University, Computer Science & Engineering

Projects of a CSE student at Marmara University

Subset simulation is a method of estimating low probability events. Here I adapt SS to perform well with correlated inputs.

Trimmed L-moments and L-comoments for robust statistics.

Interactive courseware module that addresses common foundational-level concepts taught in statistics courses.

♣️ ♦️ ♥️ ♠️ Train yourself for live Texas Holdem games by seeing the changing probability of winning as more cards are dealt.