Learning artificial intelligence can be a complex and challenging task, but with a structured roadmap and the right resources, it can be an achievable goal. Here's a detailed roadmap and list of resources for learning AI, from complete beginner to advanced.

Beginner Level

Learn Python: Python is a popular language for machine learning and artificial intelligence. Start by learning the basics of Python, including data types, loops, and functions.

Resources:

Codecademy's Learn Python 3 course: codecademy.com/learn/learn-python-3

Python for Everybody: py4e.com

Basic Mathematics: Understanding mathematics is essential for AI, as it forms the basis of many machine learning algorithms. You will need a solid understanding of algebra, calculus, and statistics.

Resources:

Khan Academy's Math courses: khanacademy.org/math

MIT's OpenCourseWare: Mathematics for Computer Science: ocw.mit.edu/courses/mathematics/18-01sc-sin..

Machine Learning Fundamentals: Learn the basics of machine learning, including supervised and unsupervised learning, linear regression, decision trees, and neural networks.

Resources:

Andrew Ng's Machine Learning course on Coursera: coursera.org/learn/machine-learning

Fast.ai's Practical Deep Learning for Coders: course.fast.ai

Intermediate Level

Deep Learning: Deep learning is a subset of machine learning that involves training artificial neural networks to recognize patterns in data. Learn about convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).

Resources:

deeplearning.ai's Deep Learning Specialization on Coursera: coursera.org/specializations/deep-learning

Stanford's CS231n: Convolutional Neural Networks for Visual Recognition: cs231n.stanford.edu

Natural Language Processing (NLP): NLP is a field of AI that deals with the interaction between computers and humans using natural language. Learn about text classification, sentiment analysis, and language generation.

Resources:

Natural Language Processing with Python: nltk.org/book

Stanford's CS224n: Natural Language Processing with Deep Learning: web.stanford.edu/class/cs224n

Computer Vision: Computer vision is a field of AI that deals with how computers can be made to interpret and understand the visual world. Learn about image classification, object detection, and segmentation.

Resources:

Udacity's Computer Vision Nanodegree: udacity.com/course/computer-vision-nanodegr..

Stanford's CS231n: Convolutional Neural Networks for Visual Recognition: cs231n.stanford.edu

Advanced Level

Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. Learn about Q-learning, policy gradients, and deep reinforcement learning.

Resources:

David Silver's Reinforcement Learning course: www0.cs.ucl.ac.uk/staff/d.silver/web/Teachi..

Berkeley's CS 285: Deep Reinforcement Learning: sites.google.com/view/cs285-fa21/home

Generative Models: Generative models are machine learning models that can generate new data similar to the training data. Learn about variational autoencoders (VAEs), generative adversarial networks (GANs), and flow-based models.

Resources:

MIT's 6.S191: Introduction to Deep Learning: introtodeeplearning.com