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!

This Repository Contain All the Artificial Intelligence Projects such as Machine Learning, Deep Learning and Generative AI that I have done while understanding Advanced Techniques & Concepts.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

Become skilled in Artificial Intelligence, Machine Learning, Generative AI, Deep Learning, Data Science, Natural Language Processing, Reinforcement Learning and more with this complete 0 to 100 repository.

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc.

A curated list of Blockchain projects for Artificial Intelligence and Machine Learning

Learn about Machine Learning and Artificial Intelligence

Translation of VIP cheatsheets for Machine Learning Deep Learning, and Artificial Intelligence

2021-2022 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics

All Stanford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine Learning, Probabilities, Statistics, Algebra and Calculus.

I am Duncan, a cofounder at Rose City Robotics. This public repository is used as an easy to update list of resources for AI developers including technical courses, books, and tutorials on artificial intelligence, deep learning and machine learning. PRs welcome!

Books related to Artificial Intelligence, Machine Learning, Deep Learning and Neural Networks

Artificial intelligence for MapleStory that uses machine learning and computer vision to navigate challenging in-game environments

Algorithm implementations and homework solutions for the Stanford's online courses

A list of summer schools on Artificial Intelligence, Machine Learning, and Healthcare

Machine Learning and Artificial Intelligence for Medicine.

An android library that uses technologies like artificial Intelligence, machine learning, and deep learning to make developers understand the content that they are displaying in their app.

My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:

Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development. bayes theorem, neural networks, and more AI.

Machine learning and artificial intelligence

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

Curated list of my reads, implementations and core concepts of Artificial Intelligence, Deep Learning, Machine Learning by best folk in the world.

A beginner's roadmap to self studying Machine Learning and Artificial Intelligence

The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.

This repository is dedicated to Python courses with a focus on artificial intelligence, data science, and practical machine learning. I have been conducting these courses in various levels since 2021.

VerifAI is a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.

Tutorials on machine learning, artificial intelligence, data science with math explanation and reusable code (in python and R)

A curated list of awesome resources for quantitative investment and trading strategies focusing on artificial intelligence and machine learning applications in finance.