Welcome to algorithms, models, and limitations. In this course, we're going to take a look at the rise of the algorithms in most of our online and increasingly offline experiences, as well as some of the limitations these algorithms start to generate as they become more powerful. In this course, we are going to cover a variety of topics, and by the end of the course, you should be able to define algorithms, recognize the core differences of modeling in theory and practice; basically what happens when we take machine learning and take it from the lab to practical applications, we will be able to explain machine learning, training and accuracy guidelines; essentially, what does it mean to be accurate and ethical in those guidelines? Then we're going to analyze the trajectory of machine learning and AI research. Where is this all headed? Five, 10, 20, and even 50 years down the road, and why is it so important to get things right now? So before we dive in, let's talk about why it's important to talk about algorithms today. Well, as I mentioned earlier, they're increasingly taking over more and more of our day-to-day lives. Algorithms when they first started to pop up on the Internet, we're really just recommendation helpers and now they're deciding more and more things in our day-to-day lives including access to capital, decisions that can really have huge implications to the ordinary citizen. It's also important to look at evolution of algorithms. So the Google Search algorithm is a great example. How has that evolved from when it first came out and the first different attributes that were put into it to today where now everything we do is now tracked, analyzed, and fed into these algorithms that all feed back into the results that we're given. It's also really important to talk about these things to distill what is fact and fiction. There are a ton of different buzzwords coming out in the industry and it seems like every company has decided if they slap artificial intelligence in front of their name, they can get more funding or get more users. So what we will distilling in this course is what it means to be intelligent, what it means to learn, and how we use these contexts within machine learning and AI. So how's this course going to work? We're going to start things off with a video lesson in each week, and then there will be a reading and then a practice quiz. The reading will just make sure that we can reinforce these concepts and see them in everyday life, and then the practice quiz is going to make sure that we have understood those concepts. Then after we go through the lessons in each week, we'll then have a graded quiz to make sure obviously that by the end of this course, you know more than when you went in. So the next question that usually comes up is how much coding will be required in this course. Coding can, in machine learning, be everything from Python to R to Lisp to JavaScript, and all those different terms and syntaxes can be confusing if you are very new to machine learning. So in this course we're going to stick to the very basics, which is something called pseudocode. Pseudocode is going to be really helpful when it comes to explaining things in machine learning because we can basically take ethics terms, something that's explainable in very plain English, and turn it into something that sounds like a sign you'd feed a computer. So for example, we will create variable x, we will take that input, set x to 10, if x is greater than seven, print yes, that'll be our function, and then it outputs yes. That's a very simple example of pseudocode and we'll come back to that throughout this course. That should make sure that as we go through, we're all on the same page regardless of what language you go and apply these concepts with after the course. That's it for now. At the end of each video, we will have a section for questions and forum discussions because obviously we don't want to have this course exist is a one-way street. We want you to be participating, asking questions, getting your questions answered, etc. So with that, let's get started. I'm excited to kick off this course with you.
