| Hello, I think it's great that you are motivated to study such topics. First, I think it would be useful for you to define what you actually want to learn. There is a difference in what has traditionally been called "AI" and what is called "Machine Learning" today. While these two fields are related and some people say that ML is a subfield of AI, both tend to focus on very different problems. Today, "Machine Learning" has taken a lot of the attention away from traditional AI (mainly due to the lack of results in making truly intelligent machines) Here are some topics I think of when hearing AI vs. Machine Learning: AI - Robotics - Intelligent Machines, e.g. for question answering - Natural Language understanding (Not NLP, I mean understanding) - Game playing/planning Machine Learning - Making predictions (Often synonymous with "big data analytics" these days) - Recommendation Systems - Finding Patterns (Data Mining) - "Data Science" techniques In Machine Learning, Python is the de-facto standard language both in Academia and Industry, mainly because because of its excellent libraries. In terms of resources, I can also highly recommend the Coursera ML class, as well as statistics classes to get started. From there you can dive deeper into any of the topics you are interested in. |