#Probability
Showing 50 of 50 repositories tagged #probability, ranked by stars
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
Become a cracked AI/ML Research Engineer
Data Science Roadmap from A to Z
The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.
The basic distribution probability Tutorial for Deep Learning Researchers
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
Teaching Materials for Dr. Waleed A. Yousef
Courses, Articles and many more which can help beginners or professionals.
All Stanford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine Learning, Probabilities, Statistics, Algebra and Calculus.
Quantitative Interview Preparation Guide, updated version here ==>
Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
Algorithm is a library of tools that is used to create intelligent applications.
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
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
Rust for data analysis encyclopedia (WIP).
Python code snippets from Discrete Mathematics for Computer Science specialization at Coursera
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.
My Solutions to 120 commonly asked data science interview questions.
Data science for beginners involves learning to extract insights from data using statistics, programming (Python/R), and visualization. Key steps include data collection, cleaning, analysis, modeling, and communicating findings. Beginners should start with Python, basic math (linear algebra/calculus), and build projects to create a portfolio.
Open-source project hosted at https://makeuseofdata.com to crowdsource a robust collection of notes related to data science (math, visualization, modeling, etc)
Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html
This repository contains notes and projects of Data scientist track from dataquest course work.
Complete mathematics curriculum for AI/ML/LLM - from foundations to research frontiers
Completion After Prompt Probability. Make your LLM make a choice
Python dice probability package.
🇦🇮 Deep Learning AI course on Coursera (Andrew Ng)
Solutions and workflow for the Bayesian Statistics The Fun Way book in Python
List of Data Science and Machine Learning Resource that I frequently use
Probabilistic Answer Set Programming and Probabilistic SAT solving, based on Differentiable Satisfiability
Data Science, Machine Learning, Deep Learning, NLP, Python & Library's cheat Sheet - Interview Questions & Notes
Using Computer with your Statistics Major Course
Implementation of the conjugate prior table for Bayesian Statistics
A curated collection of books, notes, and resources focused on mathematical foundations for machine learning, covering linear algebra, calculus, and probability. Includes summaries, practice problems, and references to enhance understanding for ML practitioners.
Learn the mathematics behind machine learning and explore various mathematical concepts within machine learning.
Practice questions for the quantitative finance interview.
UC Berkeley Data 140 Textbook
This repository contains the data analytics lessons I took from the bootcamp between 5 Jan - 4 Aug 2022 and includes 48 sessions, 10 labs, 12 assignments, 12 weekly agendas, and 5 projects.
probability & possibility
Curated roadmap for quant finance interviews (Quant Trader, Quant Researcher, Quant Analyst): probability, mental math, brainteasers, coding & high-signal resources.
It is a Probability Checker for COVID-19 , people can input values and symptoms and accr. to the data , patient will get the probability of +ve COVID-19.
Here you find CheatSheets for Data Science Topics
Curated list of mathematics, probability, ML, and quantitative trading resources for quants and algorithmic traders.