IMO, to start with AI, it's best to start as simple as possible so that you understand the algorithms that drive things that are accessible to tweak. If you are

IMO, to start with AI, it's best to start as simple as possible so that you understand the algorithms that drive things that are accessible to tweak. If you are a gamer, think about the various AI routines that control pathfinding, resource collection, and strategy for computer players in strategy games. Some of the concepts of BFS and DFS build into Minimax strategies and other types of "simple" approaches to searching decision spaces/states for optimal approaches to problem solving. This gets into the development of heuristics for approximating outcomes. All of this is fantastic baseline for understanding more complex AI and machine learning algorithms. While it's easy to jump into ML or AI at a higher level, without the baseline understanding of the search algorithms and probability landscapes that are underpinnings to advanced work in these fields, you'll probably never feel like you understand what is going on. You can find some fun tutorials here: https://www.redblobgames.com https://www.redblobgames.com The CS 188 "Intro to AI" class at Berkeley is excellent: http://ai.berkeley.edu/home.html http://ai.berkeley.edu/home.html It used to be on edx.org but I think a lawsuit about accessibility required them to remove it? Perhaps you can find it in the edX archives. Edit: looks like you can find the lecture videos and other resources on the Berkeley site: http://ai.berkeley.edu/lecture_videos.html http://ai.berkeley.edu/lecture_videos.html