Absolutely!  I'd be happy to help you with resources for an introduction to the exciting field of Artificial Intelligence programming!  And I will be mindful of your preference towards Python.

The most universally-accepted best way to learn and get started in Artificial Intelligence programming is the book ***Artificial Intelligence: A Modern Approach* by Stuart J. Russell and Peter Norvig**--often simply referred to in the field as "the Russell & Norvig book".  It is one of the most comprehensive books on Artificial Intelligence, and is regularly used as a textbook for introductory courses all the way up through graduate-level courses, at more than 1,500 universities in 135 countries.  Nonetheless, Russell & Norvig's tongue-in-cheek writing style and lifelong commitments to studying pedagogy (the science of teaching) have made the book beloved by beginners especially, as an enjoyable and gentle introduction to Artificial Intelligence.  In order to deal with the many programming languages used in Artificial Intelligence--both today and throughout its history--all programming code in the book is presented in pseudocode.  However, do not let this deter you, as every piece of pseudocode in the book has been replicated in multiple languages online, including the popular Python. You can find links to details on the book and the Python code on its website at UC Berkley: [Artificial Intelligence: A Modern Approach, 4th US ed.](http://aima.cs.berkeley.edu/)

If you work better with online instruction over books, you may prefer to start with the **Stanford School of Engineering's Machine Learning Specialization online SOE-YMLS course, taught by Andrew Ng**.  This free Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. The beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. It is a 3-part course, an updated version of Andrew Ng’s pioneering Machine Learning course, which is rated 4.9 out of 5.  It has been taken by over 4.8 million learners since it launched in 2012, a third of whom went on to begin a new career in the field after taking the course.  While the course does not cover the full breadth of the field to the degree of the Russell & Norvig book, it does focus on the most practical real-world topics in Artificial Intelligence today, including some of the best practices currently used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more).  And the course does teach how to build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.  The course can be found at: [Stanford Online](https://online.stanford.edu/courses/soe-ymls-machine-learning-specialization) and [Coursera](https://www.coursera.org/specializations/machine-learning-introduction). 

If you would like an alternative to the Stanford course, many schools offer an introduction to Artificial Intelligence course, and most of them are now in Python.  One such free online alternative to the Stanford course is the **Harvard CS50 Introduction to Artificial Intelligence with Python**.  The course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.  This course has a bit more breadth like the Russell & Norvig book, but in an online format like the Stanford course.  Therefore, Harvard's course may be best suited to a beginner who does not yet know exactly what aspects of Artificial Intelligence appeal to them yet and thus want a broad overview, but would nonetheless prefer an online course over the book format.  The course can be found at: [Harvard's CS50 program](https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python), and--like the Stanford course--costs if taken for credit or certificate, but is free to audit or view the archives.

If a course or a book seems too much to start with, or you just want to dive right in with a project, then the **code.org AI and Machine Learning tutorial** is another option.  This unit is a hands-on introduction to developing a machine learning model with tabular data. It explores how computers learn from data to make decisions, then shows you how to develop your own machine learning projects around real-world data. The tutorial culminates with designing your own machine learning app to solve a personally relevant problem.   Code.org is an education innovation nonprofit dedicated to the vision that every student in every school has the opportunity to learn computer science as part of their core education. They expand access to computer science in schools, with a focus on increasing participation by young women and students from other underrepresented groups. It is the leading provider of K-12 computer science curriculum in the largest school districts in the United States; and also organizes the annual Hour of Code campaign, which has engaged more than 15% of all students in the world.  This tutorial will not have the breadth and depth of the book and courses, but it is a great option for a younger beginner that is eager to get hands-on as quickly as possible, or as a shorter introduction to get your feet wet before proceeding to one of the other options. Also, this tutorial uses easier-to-understand Lab App instead of Python, which is best suited for younger learners. However, Lab App code is exportable, and the principles learned could be used to build the project in Python.  This project tutorial can be found at: [code.org AI and Machine Learning](https://studio.code.org/s/aiml-2023).

For tools to assist you on your journey, also check out the **Artificial Intelligence section of the official Python Wiki**. It is a repository of further information and links pertaining to the practices of Artificial Intelligence and Machine Learning, specifically for Python.  The list can be found at: [Python For Artificial Intelligence](https://wiki.python.org/moin/PythonForArtificialIntelligence).

I hope this helps you with the best resources for an introduction to Artificial Intelligence programming in Python that suits you.

Do you have any questions, or is there anything else I can do to assist you?
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