There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
These days you'll be hard-pressed to find someone who hasn't interrogated Siri (or Alexa), enjoyed the movie Netflix suggested, or fallen victim to purchasing that additional item Amazon recommended--all of which are only possible due to artificial intelligence. AI has been a field of study as far back as the 1950s, but advances have skyrocketed in recent years. These days AI is everywhere and has increasingly become part of all of our everyday lives. Thanks to AI, once tedious tasks are now simple, single-click activities. And as technology becomes even more pervasive, it will only continue to impact our personal and professional lives.
Python is a dynamic modern object -oriented programming language. It is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer. It is also known as a general purpose programming language due to it's flexibility.
I can't offer much in terms of other entry level recommendations, but I can recommend you learn to utilize the resource pages on the coursera course. The way the andrew NG course is set up is that you more or less try to have an idea of how these algorithms work at a conceptual level through the videos, then when you go to programming assignments, you can skip a lot of the prep work and focus on implementing the machine learning algorithms. Now those algorithms might be a little hard to follow at first, which is okay and expected, and that's where the lecture notes and/or wiki come in. From the wiki you can more or less translate the math formulas into code syntax and the assignments are more or less complete. The weeks build off each other so as you learn how to do one part, they do a little less prep work for you so you have to learn how to do another part, and so forth.