AI With Python Full Course 2026 [FREE] | Learn Artificial Intelligence With Python | Simplilearn

[music] Hey everyone and welcome to this Python for AI refresher course. So in this course we will take Python from something that may look really confusing to something that you can understand and use. Whether you want to get into AI, machine learning, data science or just strengthen your Python basics. This course will help you build the foundation step by step. So first we will understand why Python is so important in AI. Then we will set up tools like VS Code, Jupyter Notebook, Anaconda and Google Collab. After that we will cover Python basics like variables, data types, operators, input and output. Then we will move into lists, tpples, dictionaries, sets, conditions, loops, and functions. And by the end of this course, you'll not only know Python basics, but also understand how these concepts are used at the starting point of AI and machine learning. If you're interested in mastering the future of technology, the professional certificate course in generative AI and machine learning is the perfect opportunity for you. So offered in collaboration with ENICT Academy, IIT Kpur, this 11-month live interactive program provides hands-on expertise in cuttingedge areas like generative AI, machine learning and tools such as chat, GPT, DLE, and hugging face. You will gain practical experience through 15 plus projects, integrated labs and live master classes delivered by esteemed IIT faculty alongside earning a prestigious certificate from IIT Kpur. You will also receive official Microsoft badges for Azure AI courses and career support through simply learns job assist program. So hurry up and enroll now. Find the course link in the description box below. So before we begin, here's a quick quiz question. Which Python concept helps us reuse code instead of writing the same logic again and again? Is it a variable, B function, C comment, or is it D operator? Drop your answer in the comments below and let's get started. So, this first lesson is all about an introduction to what Python is. So, if you're completely unfamiliar with it, totally fine. We will uh get you up to speed and talk about the fundamentals and how to set everything up on your own computer and talk about the various ways to um utilize Python that some of it will involve a setup you can do on your own computer. Some of it will involve some cloud resources um so that you don't need to set anything up on your computer if you don't want to. Um we'll have options there which will be nice. So I will show us those and walk us through those. But this first lesson all about the basics uh and getting set up. So um what's interesting is like at the beginning of every lesson we usually have this uh kind of um engagement or discussion. Uh but you know we've I kind of already asked you guys about this of uh uh if you're familiar with programming, if you're familiar with Python. Um but one thing I want you to think about a little bit is that um especially as we go along and learn about what Python is is why is Python the chosen language for AI? So why is it the one that everyone uses uh to do AI? And I think what you're going to learn is that it has a really amazing ecosystem that has been around for a long time that um supports AI in particular. So, Python is the go-to for anything AI, data science, machine learning, anything in that sort. Uh, because it's been used for so long for that and it has such a uh community and ecosystem around it. That's something we're going to learn. It's also really easy to learn and use, which makes it nice to to be uh kind of an introduction to the field. It doesn't take a lot to get started in it. because it's so easy to work with. Um, I can tell you as someone who's gone through that experience, like I studied mathematics in college and in graduate school and studied like probability and statistics, but I was able to teach myself Python primarily and use that to get into kind of data science and machine learning in the industry. So, and I think that's a common story is people and I've seen that from many learners coming from uh different backgrounds. Uh they've been able to pick up Python pretty easily because it's a very easy language to understand and and syntax of it and there's so many tools within it that make it really easy to work with. So, um I promise it won't be as uh daunting as it may seem even if you're coming at it from zero experience. Uh, I think you'll find this is the perfect way to get into programming and get into data science and and AI and machine learning because it's so easy to pick up and learn and it has such a nice rich community ecosystem. So, just wanted to mention that. Okay. So, some of our objectives for this first lesson will be to talk about programming languages in general and um programming in general. So maybe you know more generic than Python just you know what are what do general programs look like what are some of the building blocks of programs that are important what are some of those uh key principles of programming that we will want to follow as well even if we're doing Python for AI purposes um so just talk about programming in general and then kind of zoom in on Python as we go along. One of the things we'll be interested in doing is just getting you guys set up. So talk about how we can configure Python for you to use on your own machine. Um but also have some options that don't require installing anything on your own machine. Uh which is nice. Um and then as I said, we'll kind of zoom in on Python, talk about its benefits, uh some of the nice features. I've kind of already mentioned it. Really big community around it. Easy to learn. We'll just talk about those more in detail. Talk about um why it's so popular in the AI world. Um, and then we'll get into some very fundamental things specific to Python. So once we talk about the background, get you guys set up, we'll go into uh some of the syntax basics, things like identifiers, things like indentation, comments, um, some of the basics of the code that are going to be important for you to kind of get started with. Um and then talk about some of the basic data types that Python offers to manipulate and work with data which of course is important um when you know as we go forward and and do anything with data which of course with AI we will be interested in doing um but that's these are the objectives of just the this first lesson. As we go forward we're going to learn about many other basic topics within Python. So things like how to write functions, how to build objects, how to manipulate our flow of the program with like things like if else statements, things like loops. We'll learn all about that in kind of the next lessons after this one. But this is all the content for this lesson. I anticipate today we will get through all of this today and then get into the second lesson which will um get into those kind of if else and loops. So we'll we'll get we'll I'm sure by today we'll get into those. All right, any questions on kind of what we're going to learn in this first lesson? So mainly trying to get you guys set up, give you some background on Python and then towards the end of the lesson um get into some basics of the syntax is kind of the goals I would say. Okay. Okay. So when we talk about programming um what do we mean by programming in general? It's really uh synonymous with instruction. So programming really means giving or writing instructions for a computer to perform tasks. Um so these instructions we write down in what we call code. But those those are just telling the computer what to do. And of course the computer's not going to do anything unless we write down these instructions. So these instructions can do really powerful things. They can power, you know, whole applications, things that we use every day like Microsoft Word, PowerPoint, Excel, those kind of things. Um they can automate tasks. They can um power websites. Um they can do AI, right? So we can have um things like chatbt and Alexa and Siri, etc., etc. Um these are all powered by instructions telling the computer what to do. One of the things that we will get better at as we go along is figuring out how to write these instructions in Python. Python is going to be the language we write those instructions in um and and they will be executed by a Python um program. But we should think of programming in general as just instructing the computer what to do just at a high level. Right? So when we talk about these instructions, they have two ways of being executed by the the computer. Um and roughly these break down into what we call interpreted languages and compiled languages. So that the code that we write which is um representing the instructions that we write can be executed um in one of these two ways. Let me start with the left. So the interpreted languages. This means that the computer is literally executing the the instructions line by line by line when we run the program. So there is no translation of anything. It's just literally taking our instructions and running it line by line, instruction by instruction essentially. Um, now the advantage to doing this is that it's uh easier to debug because the instructions are going to be executed one by one. So it can hit an error pretty quick. If there's a mistake in one instruction, nothing else will run. Um, however, it's also slower because we're going to take it one instruction at a time. Um, and so the the uh this way of running programs tends to be slower, but it's also easier to work with, which is why we're so interested in Python. It's in this bucket of what we call interpreted languages. So, a lot of scripting languages find themselves in this bucket of being executed one line at a time. No translation needed by the machine. It just reads our instructions and executes it. The thing that does the execution is called an interpreter. Um, and Python has an interpreter that we will get you guys set up with on your own machine that can execute Python code. So you need an interpreter. The interpreter just executes your instructions line by line by line. Um, so some examples would be like Python. That's what we're going to study in this um entire program. But there's other languages like JavaScript, Ruby, um Pearl, many others that are uh interpreted. They require an interpreter, but they execute line by line by line and there's no intermediate translation of anything. Um it's kind of executed as is. Now, contrast this with compiled languages, which are uh kind of a different piece. they these these instructions have to be translated into something the machine can understand in order to execute. So there is an intermediate step of what we call compiling the code um into uh basically a translated version of your instructions so that the machine can execute it. Now there's a trade-off there. Doing that can make it more difficult to develop and it can take longer to debug because you have to go through this translation step every single time through the compiler. But when you run the code because it's already been translated into this machine format, it's a lot faster. Um, so some examples of languages like this are C, C++, Java, um, Go, but uh, we won't really be working with those. We'll just be sticking with Python. But if you have experience with those languages, those you're probably familiar with this, you have to compile the program first before you can execute it. But we are going to be in this interpreted world. If you know and it's okay like if none of this makes sense, that's okay. Just understand that um generally interpreted languages are going to be more user friendly because they're they're easier to execute. They don't require as many moving parts as what a compiled language would require. which is nice for us, right? Nice for Python. That's what we're going to be interested in working with. Uh kind of um yeah, they're kind of rel So, so the question is are JavaScript and Java related? Kind of. Um, JavaScript is kind of like the um the the scripting version of um some of the same concepts we see in Java, but Java is the compiled um it it requires a a special kind of what's called a Java runtime, which is a a compiler to translate the Java code into um machine code that the Java runtime will execute. JavaScript is not like that at all. It can actually be ran in a web browser which is um JavaScript usually powers a lot of like front-end websites are usually powered by JavaScript and Java usually powers more like backend um applications like actual software programs are usually would be coded in Java. JavaScript is going to be used more for like building a website. But, you know, I'm not an expert on that really, but that's kind of my understanding of it. And if anyone is an expert on those differences, feel free to let us know in the chat. But, uh, that's my that's my basic summary of that. Okay. So, we have interpreted languages. That's where Python falls under. So, it just um summarizing that, it's going to be easier to work with those, which is great for us. That's another reason why Python's so easy. It's interpreted, meaning that everything executes. We don't need to worry about compiling things, which is nice. Um, but also in terms of programming, there's also uh categories of how the instructions are written that you can bucket different languages into. So for example um some language are are more um procedural in nature meaning that you write out all the instructions exactly kind of line by line by line. You don't really organize things at all in your instructions. Um so some examples would be like C and Pascal are more like that. Um then on the opposite end of the spectrum is kind of object-oriented in which case you uh build your code and organize it around the idea of everything being an object. And so some uh Python actually falls into this category where um uh most things in Python are objects and you manipulate objects and objects have data to them. They have things they can do and interact with other objects. Um, so think of it just as a way we will organize our instructions. Python allows us to organize it around the concept of an object. We'll learn about what that means as we go along, but just realizing that some programming languages break down along these um kind of buckets here. Um, Python is also a scripted language, meaning you can write out your code in a individual script and you can e that you can have an interpreter that executes that script. Um, so you don't need to organize all your code inside of an object. So for that reason, Python super flexible. That's another reason why it's so nice to use. It actually falls into both of these buckets on the right, which is very convenient. We can have basically this means we can have a lot of organization or very little organization depending on how we want to set it up. Yeah, Roberto. So even though there are different types so Java is compiled and Python is interpreted um they are both object-oriented meaning so think of the this slide as telling you how the instructions are organized. So how they are executed is different. So, Java requires a a compiler to execute things. Python requires an interpreter. This is more about how the instructions are organized. So, Java and Python both allow you to organize your code into objects. Um, but what's nice about Python is it also falls under the bucket of scripting, meaning that it allows you to organize things into scripts, which is less organization than it would be in into objects. We're actually going to learn about objects later on in a future lesson, like how to build objects and what they mean. So yeah, even though they're different, they're both object-oriented, which just means that you can organize your code into objects. Python allows that. So does Java. So does C++. Uh many many languages allow for um organizing your your code into objects. So we're going to learn about that. It's It's not that one's better. They're just um I I would put them at different So, let me draw this. I would put them at different spectrum, different ends of the spectrum on organization. So, scripting is very loose. Basically, you it's more like a an individual um uh set of instructions to do one task. you can just have and you can have many individual scripts to do many small tasks. Um, and then on the other end of the spectrum, think about it as like you've organized your cabinet into many folders and many like uh you know many pieces of organization that are we would call objects. Um so objectoriented programming OOP is kind of on the other end of the spectrum when it comes to like level level of organization. Does that make sense? So scripting very loose. It usually scripting is is um reserved for like one task and it's um you're just writing out your instructions to accomplish that one task. um which is helpful for like automation of things because you're going you're usually automating like a single task. Um so it's very loose. It's not very organized and nothing is organized necessarily in objects. Um very loose organization. Object-oriented is much more structure to it and things being put into objects um in order to manipulate and work with objects throughout the program. Yeah, it's not that one's better. I think it's more just use case dependent. Um there are times where it actually will benefit us from using objects. Um and I think the thing to pay attention to on this slide is that look at where Python falls into. It actually falls into both. Meaning that we can have things very loose and easy to work with because scripting usually will be faster and easier to just write something to to accomplish one task. But we have the flexibility to organize our code into objects if we want to. which will be better for bigger tasks that require more organization like training a neural network or building an LLM. Those bigger tasks would benefit from more organization. And then uh finally on this slide um there are languages that are built on the concept of um their entire way of writing instructions is more in a functional way meaning everything is based on operating uh functions and variables. Um and so there are some languages like that. Has scholar ones um but that is can be very difficult to learn. It's it can be difficult but very nice in some ways because uh it can be very natural to think of um you manipulate like giving instructions to a computer in a functional way. Think about it as like applying a function to a variable. Um that makes sense but writing your all of your instructions in that way can be kind of difficult to learn. So for that reason I think these languages are more difficult to learn but they can be very powerful. Um and they find themselves very useful in like operating on big data. Um so if you ever heard of like Spark um Spark operates with uh Scola for instance um but uh we won't really focus on functional. It's kind of its own paradigm. Um but uh again like Python is where our focus will be. It allows us to be really organized, loosely organized. Nice flexibility there. So, so far based on these two slides, I'm showing you that Python is interpreted, which is easier and faster to work with. Um, not faster to run, but faster to get up and running because you don't need to compile things. That's nice from our perspective. And it's also has very good flexibility when it comes to organizing our instructions, organizing our code. could be very loose in scripts could be very structured in in objects. Okay. Okay. So generally no matter how uh no matter what language it is um when you process those instructions generally things are going to be organized even if it's in a script or if it's um you're generally going to have the very beginning of the program um kind of setting up the input then the middle of it really processing that and doing something with that. So that's usually like the bulk of the logic is in the processing phase and then generally you're producing some output. So that could be like a model prediction, that could be um a a graph that you've built from your code. Um whatever that output is, but generally it flows this way. This is this is makes sense, right? Of course there's input, you're manipulating that input in some way and then you're producing some output. I think that all makes sense. That's a very logical way to flow. Um now that's not to say that within this processing step there may not be um iteration like of course there may may be times where we need to as part of the processing kind of iterate and do multiple passes of processing. Um so the processing could be a lot. We could be doing a lot. We could be doing a little. Just depends on what we're actually doing. So, if we're reading in some data as the input um and then we're just doing some simple um slicing and dicing of it, that's some easy processing and maybe producing a graph or producing a metric, something of that sort, that's pretty easy to do. But if we're training a neural network or training a model, the processing step can take a while and it may, you know, be very iterative in nature. So it just depends on what we're doing and those instructions but no matter what most of our programs will flow in this way kind of input processing output. It makes sense very logical. So what are some principles that we should abide by when we're writing our code? So this this would really be for any language but of course for Python that we are interested in. Um so something we're going to be interested in doing is um basically avoiding repetition where we can. So instead of having copy paste everywhere, we will generally favor organizing our code to some degree. Meaning we will utilize functions where it makes sense and objects where it makes sense to organize things. And also instead of um having very repetitive code, we will favor using uh loop structures that can iterate over um things many times instead of us us having to write all those out one by one by one. So we're going to learn about these tools that we have at our disposal, but they will help us organize our code, avoid repetition all over the place. One of the things we want to avoid is having the same code repeated all over the place. If if we find ourselves doing that, we should really put that code into a function or maybe into an object so that we can reuse it. So, we're really going to favor like reusability of things, re recycle, reuse, you know. So, we're going to learn how to do that, how to build functions, how to build objects. But that's something we're going to favor uh when we're when we're programming. It's something you should be on the lookout for. If you find yourself writing the same code over and over just in different spots, um that's probably a clue you should organize that into a function so you can just call that function wherever you need to rather than copying all that code. Okay, so we're going to avoid repetition. Now, the the reason we're going to do that is to uh you know keep everything simple. We want to make sure things are clean, simple, understandable. Um, we don't want to ha we don't want to have overly complex things that are very difficult to follow. So, one of the things that is going to be really nice about Python is it lends itself very well to being simple because it's going to be so easy to actually read and understand um, you know, understand what's going on. But one of the things that falls in line with this is like um for instance naming things appropriately. So instead of just calling everything in our code like X Y and Z if somebody comes along and reads oh I see your code has an X Y and Z that may not make sense. You know we would want to be more thoughtful with the names of our variables and names of our function. So instead of XYZ maybe we would use something like name or place or you know something appropriate to identify this is what this is. So think about that when you're writing your code is try to make it understandable. Name things that somebody else reading it would understand what it is if they see that name. So that's that's a mistake I see a lot of people make when they first start. It's okay like when you're first getting started and practicing to name things like X, Y, and Z. I think that's fine. Or like ABC. Um, but does that make sense? Like if somebody else was reading it, they see XYZ in the program, that may not make sense, you know. So, but if it has a good name to it, you could say, oh, like I see this is somebody's name that this variable is referring to or this is um a particular object that this is referring to. Um, it's not just kind of an abstract X or Y or Z. Yeah, [laughter] no spaghetti. Yeah, that's that's what uh that's what a lot of people refer to that as. Uh just sloppy, unorganized, um hard to understand code. One of the things that's great about Python is it's naturally very understandable. So like I don't think we will have that issue as much as if we had other languages, but it's still possible. So these are things we'll learn as we go along. I'm just trying to get it into your mind a little early here. Name things appropriately is main one of the main pieces of advice I can give here. so the next tip is to organize things. This goes along with avoiding repetition. So organize um let's put things into functions. Let's put things into objects where it makes sense. If we know we're going to reuse that um let's put it into a function. And so we're going to learn about how to do that. But generally this is good practice if you find yourself writing um uh code to do something and it turns out to be um it turns out to be uh something you know you're going to reuse or it turns out to be more than a handful of lines of code. Generally you want to organize that into a function so that uh it's clear this is what this code is doing. This is what it's responsible for. it's obvious um you know that it's organized into into uh that unit of work essentially. So we are going to practice this. This is something we're going to get good at I think as we go along because we're going to favor organization where it makes sense. Okay. So readability. One of the things is using meaningful names. I kind of already mentioned that. The other thing is using good comments. So, we're going to learn probably today how to make comments in our Python code, which is going to be helpful to orient yourself or another reader of it to, hey, this is what this function does. This is what this line of code is doing. Um, I can't tell you how many times, you know, people write code and then it they themselves come back to it a week later and have no idea what it's doing. That happens all the time. It's even happened to me. So, uh, comments are your friend in that regard. and that um they don't really cost you anything to put comments in there um to say to to kind of highlight this is what this piece of code is doing and you can make a note to yourself right within the code. That's what comments are. They're basically notes to yourself. Um so we're going to learn about that today. How to write comments and and what that looks like in the code. The other thing is indentation. you know, Python supports uh indent like you have to indent. So, that's not really going to be an issue. Some languages don't really support that, especially the compiled ones. They don't enforce strictly indentation. They enforce other things like braces and and semicolons and such, but um our our Python code will be properly indented uh by necessity because otherwise it won't work. So, um, that's something we're going to learn about too today is how we indent things and why that matters. We'll talk about that. I see a question from Sherry. Is Python a program that can be programmed with simple language? Yes, it's very easy to uh it it's Python is a very natural language to program in because um yeah it's very simple uh simple languages used all over the place. I think it's going to be really easy to learn. I think it'll be really easy to pick up. At least that's my hope and I think it from my experience it is. As I said I was someone who did that and I've worked with many learners who've done the same. So yes, I think it'll be pretty easy to pick up. Very simple. Um, and then the other thing is we can do uh we can find our errors very quickly. Now because this is an interpreted language, we can run things one line at a time and we we will quickly hit errors uh early on in our code if if we have them. So this will be nice and Python provides really good um error messages um to say hey like this is what's wrong with your code you should fix it this way um essentially like giving you a clue into what needs to be fixed. Um so so this is something uh that we will practice with as we go along is kind of um finding errors and what to do with them. Um, but because it's interpreted, we will run across those very quickly. Unlike with compiled language, which is harder to debug because you basically have to compile everything, hope that it compiles. If it does, then you have to run things. Um, it just takes longer to get through that debugging phase. But with the with Python, it's very quick. You get a very quick feedback loop on if your code's working or not, which is nice. A lot of votes for C. I agree. C is the correct answer here. So the interpreter is the thing that will execute the code line by line. So it doesn't do everything at once. It actually goes line by line, which is why you can stumble onto your errors quickly because if you're going line by line um and you have an error on this first line, you're never going to reach these other lines, right? You're it's just going to show you this is where your error is. it's on line 101 or whatever it is and you know it's going to show you where the error is. So it's going to go one at a time and execute those. Um it's not going to convert the code into machine language. That's what a compiled language would do, not an interpreted one. Um and uh they do require an interpreter. So D is just completely wrong. It's the opposite of that. It does require it. So the interpreter is the thing that is executing the uh code line by line. So what is Python in particular? So it is a as we've already seen an interpreted language meaning that it requires an interpreter to execute it. It's going to be executed line by line by that interpreter. Um it has capability to be object-oriented. It also has capability to be scripted. Um which is just in relation to how it's organized. One of the really nice things is it is what we call dynamically typed or what you would say dynamic semantics. We will see what this means. But basically it means that we don't have to declare what every piece of uh what every variable or every piece of data is inside of Python. We can let the interpreter interpret that which is nice. It makes things really easy to work with. We don't need to say okay this is an integer this is a floatingoint number this is an array this is you know with a lot of program especially compiled languages programming languages you have to do that because you have to tell the compiler this is what this piece of data is but with an interpreter the interpreter can as the name suggests interpret that it doesn't need to know what everything is in terms of its data type which is which makes it really easy to code. On the cons of that, it can make it more prone to error because you're not really enforcing types. So, there is somewhat of a trade-off there. But, um, for our purposes, the dynamic semantics make make it so that um, the interpreter can dynamically understand what data is um, based on how it's being used, which is great um, for us. like it makes it just quicker to get up and running and started and and working with data. We don't need to declare what its type is which is um static semantics. Um now Python itself amazing programming language that's used across many different applications um such as data science, automation, machine learning, AI. It's also used in [clears throat] to build software even um not sure if you guys know this but there's um some really famous software that's written in Python. Um, one of the most famous is Instagram at Meta is completely coded in Python, which is it's over like 20,000 lines of Python code, which is pretty amazing. But um so of course it's been really um heavily used in AI and machine learning and such but it's also as a programming language been used for other things like more pure software applications which is what makes Python really nice is it's so simple so easy to learn. Um so for that reason uh it is going to be great for us to get started with especially if you're coming in with basically no programming experience. The other thing about Python is it has uh as I said earlier like a really big ecosystem uh meaning that there's many different packages and modules within those package packages that do things already. So we don't what's great about Python is we won't need to reinvent the wheel on so many different things like if we need to build a plot if we need to train a model and and use a specific type of model that likely already exists in a package somewhere. And what's great is they're almost always open source meaning we don't have to pay for anything. You just use it out of the box which is fantastic. So there's within Python there's so many ways to do things especially in the AI and machine learning world that we'll just borrow those and use them in our own code um which helps uh you know with um getting up and running very quickly. We don't need to reinvent things. We can just use things that already exist um which is fantastic. So that ecosystem really benefits machine learning AI. Um because they they already exist. We don't need to spend our time rewriting all those things. Um and so that's something we're going to learn as we go along is like how to install those, how to import those, how to use those in our own code, those those packages that already do something for us. So we don't need to come up with it on our own. we just need to use it properly. Okay, so there's a little bit of history. Python was first invented in the late 1980s by a guy named Guido Van Rossom in Amsterdam. Um, where it gets its name is after the old comedy series, you guys might be familiar with it, the Monty Python Flying Circus Show. Um, and so that's where it's got its name. um you know it was first created then but has since taken on a really big role in the especially you know I keep saying in the AI community so much so that it has its own software foundation that kind of is responsible for maintaining it they meet regularly they come up with improvements um they come up with new versions of Python uh for example Python 3.14 just released in October which is a major release. Uh they hadn't had one in a while and that one is uh 3.14. So it's kind of known as Python. Um which was a big milestone. Um but you know they have uh they've had many different versions over the years. It's been maintained and developed by this software foundation. Um and people are actively working on it at many large companies. So for instance, Meta has a big group that is working on um Python improvements. Microsoft as well, um Google, all of those guys have groups kind of working to improve Python because they all use it. And so what they typically do is work on it, open source it, and then the community gets to use those tools, those packages, those tools, those improvements. Um so it's it's actively um utilized across many big companies actively uh maintained by them or contributed to by them. So that's that's really great. Um you know Python was originally derived from other language um other languages uh as kind of a trying to find like a mixture of some of the best of all worlds. But its main like driving force in why Python came to existence from these other languages is it just its ease of use. People really wanted something like super easy to get up and running and something really natural. Um and so we will as we start learning the syntax of it I think you guys will understand why it's so easy. But um that's that's what led to the inspiration is just people wanted something easier to work with, not as not as uh strenuous to kind of get up and running. What open source license is it? Um that's a good question. I think it's the MIT license, but I could be wrong on that. You could look it up if you go to python.org. Yeah, if you go to python.org, or I think it might talk more about what the uh license structure is there. I want to say it's MIT open license, but I've I'm really not 100% sure on that. Okay, so what are some of the benefits of working with Python? And these are things you will experience as we go along, but just wanted to call them out. Um the flexibility of it as I said it can be really organized into object-oriented or it can be loosely organized into scripts. So that flexibility alone is really awesome. um which has allowed it to power many different things like um APIs, web pages, full-blown applications like Instagram, um chat, GPTs, like actual uh AI, LLMs. Um you know, it has so much flexibility there to power so many different applications. Um probably the biggest benefit, especially to us, is its ease of use. Um uh oh, thank you. Some Tim just posted it. It's the the GNU uh public license. Yes. Oh, never mind. It's a Python software. It has its own. Okay, perfect. Thanks for sharing that. Thanks for sharing that. Yeah, I wasn't completely sure which which license it was, but it is open source. Um, and people do make their own kind of derivations of Python. But as I was saying, one of the benefits of Python is how easy it is to learn. I keep emphasizing that because it's true. Once we get into it, you will see this. I promise it'll be easy to learn, easy to pick up. Um, and it's designed in that way. Designed to be very minimalist as a language, which is great. um it has a lot of things that come with it and it's kind of built into Python, a lot of capability. So we call that the standard library. It's just the things built into Python. It has a lot of capability out of the box. Um you know, not only that, but it has a large community that's developed so many different packages that do things for us, especially in the AI world. So that's another great thing kind of a robust community developing these packages that help us get things done. Um readability. So because the code is so simple, it's also easy to read. So you can usually read other Python code and quickly understand what it's doing which you know makes for easy um easy understanding of other people's code easy understanding of code in the community and kind of almost like it's selfdocumenting because it's so easy to read. So that that simplicity that ease of use lends itself well to being really readable. You can usually just take a look at the code, easily read it, understand what it's doing, which is great, like great for you guys learning, great for taking a look at the demos and examples that we will do. They're very readable. Okay. So why has Python really dominated AI? So this is a valid question like even so it's used for many different things. It's a programming language so it can build application and I've given you the example of Instagram and there's many others um that are built off of Python code. Why is it so useful for AI in particular? mainly uh some of the reasons we've already talked about mainly how easy it is to use lends itself well for AI because um that has allowed people to kind of quickly get up and running and test out their algorithms, test out their models just really quickly with Python. That's great. The other things listed on here are certainly big reasons as well. So for example, it has so many community libraries, those those packages that um have AI models and AI tools that we can reuse that people have built these up over years and years and years. Um so it's to our benefit to reuse those and not have to reinvent everything. We can get quickly up and running with those which would be great. The other thing is Python it lends itself very very well to working with data in general very easy to work with data very easy to load it in from external sources query it work with it visualize it Python is so adept at that um so that's what makes it really nice at doing machine learning and AI because so much of it is manipulating data so um for that reason alone Python is so popular in the AI community just because of its ability to work with data It's so easy. This is something we're going to really focus in on like in our next course when we talk about data science, but um just the ability and the power of it to work with data makes lends itself well to AI uh capabilities. Um the other thing is I mentioned the rapid prototyping. You can quickly build a model in Python because the code is so easy and there's so many libraries already can quickly prototype. Um it has obviously a big community around it that's building out these packages, writing documentation, maintaining it from an open source level. So that's another reason it's very popular. Um Python's also used with other technologies. So it does have capability to integrate with other languages. So for instance, Python can one of the most popular integrations is Python can work with C and C++. So sometimes that's necessary to integrate with those to do certain things. Um so Python has been extended to work with other languages. So sometimes there's other uh necessary support from other like things in other languages that are necessary to power something in AI. Um for example working with GPUs and doing things in deep learning. Um there's been a lot of integration with uh working with um C tools. Now will we do that? No, it's already been done for us and some of these packages. But um the pure ability of Python to do that is really powerful and it gets taken for granted honestly because you don't see that. It's underneath the hood and it's abstracted away from you when you work with those Python packages. But there was a lot of work that went into it to integrate it with other kind of other programming languages. So as an example, like I mentioned the Instagram one. So Netflix for instance, all of their recommendation is powered by Python. So when you open up Netflix or really any streaming service for that matter, they're going to use Python to deliver those recommendations and produce those personalized recommendations. um Spotify as well for like music. Um nearly all recommendation algorithms are written in Python. And in this program, we are actually going to learn about recommendation systems. So that'll be pretty fun. Way down the road when we get into machine learning. We'll talk about how do we build a recommendation engine, but um they're all done through Python for for example. So really cool uh use cases there. So, one of the things I wanted to address is how AI itself is changing coding. So, you guys may be aware of this, but obviously there's been a huge um kind of explosion in generative AI tools that can help write documents and write emails and write text and all these things. One of the things they can do is write code. So, um, one of the big areas where AI is changing coding is it's an its ability to generate code for us. And so, um, throughout this program, like we won't shy away from that necessarily. And I encourage you guys to use AI tools as you see fit to help your own understanding and help your own productivity. um you know we still will go through the fundamentals so you can understand it but the AI tools can definitely be a supplement to help um it's just that I think you guys will understand it better going through the examples that we do we do together and so that when AI generates code you will be able to understand it and also be able to debug it right because it's not always going to be perfect so that's always the catch with AI is that you know it doesn't always produce perfect answers. Um but the at the very least we will be able to you know debug things and understand things better so that uh we can catch those errors. Um so obviously like AI is also besides flatout generating it it's also suggesting what should be there. So, uh, some of the code editors really do a good job at that, suggesting things, um, picking up on what you should produce next. That's going to be, um, very interesting as we get into, uh, some of the platforms that you guys will work with to write your Python code. They will have that ability. Um, so, uh, the other thing is like there's some cloud tools that, um, don't require writing much code at all and they can just do things. So, in other words, you can power them by prompts. You're not really writing code. You're just writing natural language and then they do something. Um, they generate the code in the background and they execute something. Um we will learn about those things uh later on in the program especially because we we will cover generative AI in the future um towards the end of our program. So if you're wondering like are we going to cover LLMs? Are we going to cover how these things get generated? Yes. It just will be um later on in the program. Okay. A lot of votes for B. Yeah, pretty unanimous on B. I think I agree with it. Yeah, B is definitely the right answer. So, all of the recommendation systems which we will learn how to build ourselves later on are written in Python and um they uh are machine learning models that make the recommendations and that machine learning is driven by data um and all of that data is manipulated in Python um and used to train uh models that do the recommendations. That's all happening in Python. So, we're going to talk about getting you guys set up on your own machine and talking about the different development environments we can use to actually work with Python code. Um, before we go into that, any questions about anything we covered so far? Everything's good so far. Yep. And you know again if you have experience in Python I recognize that it is going to be a little slow in beginning. Um it's mostly to get us really oriented to some background around Python and get us set up and then we will be doing you know uh getting into the syntax and all that uh coming up shortly. So we will actually be learning Python specifics coming up soon. But you know we're going to um get everything set up first. All right. So, let's continue then. Thank you guys for that. So, um it turns out that there are many tools in the community for developing Python code. And so, um you might hear this word ID. It is short for integrated development environment. This is a piece of software that helps you write and test Python code. So, and there's many out there. There's a bunch on this list. We are going to focus on a few options. There's even more than what's on this list, but we're going to focus on a few options. These IDs are designed to really help you write Python. They provide many tools in the background that make your life easier when you're working with Python. So, for example, they can provide syntax highlighting. They can tell you when you have a syntax error. Um, almost like a spell check for Um, they can help you run Python code right within the window. Um, they can help you organize your projects. Uh, they can do a lot of different things. Um, and so there's many tools out there that can do it, and it's really a personal preference which one you use, but in this program, we're really going to focus on a few of them to to showcase those options because they're very popular options. Um, and then, uh, allow you guys the flexibility to choose which option makes the most sense for you. So, generally, that's going to be mostly a a preference. um mostly a preference as to which one you're the most comfortable with, but I want to give you guys the option to uh explore the various options that are available. Uh Roberto, is there one that stands out as an industry standard? Um there's a couple that you see like honestly the two of them that we will study uh in this coming up in the next few slides are the industry standard which are going to be VS code Microsoft VS code and then Jupyter notebooks. [snorts] So these two are going to be uh ones that we will study in particular and use throughout. so so yes we will those will be industry standards. PyCharm's also very popular. Um so I don't want to rule out PyCharm. I know a lot of people who use it. So um I would encourage you to explore PyCharm as well if you want to but we are not going to do that uh in in these slides but um I would check it out and see if you like it. Um it's another very I'm putting a an asterisk next to it because I think it's one of the more popular uh yes uh yeah we're going to do descriptions. um requirements. Uh I'll try my best to give those but honestly the requirements will be given when you install them. Um so the other thing I want to say is we will have a couple options that don't require you to install anything. So I'm going to showcase those as well. So there's a couple options that are um we won't have to install anything because they're going to be cloud-based. Okay, I'll show you those. Okay. So, but yeah, VS Code, I think VS Code and Jupyter notebooks are are probably the industry standard most popular uh idees. Okay. So, what we would recommend in this program and the ones that we will use the most uh throughout are going to be these three. Visual Studio Code, also known as VS Code, Jupyter Notebooks, and Google Coll Collab, which is Google's hosted um Google's hosted version of notebooks essentially. Um so I will showcase each one of these and give you some examples of how to set it up and examples of how to work with it. Um, and that's what we'll do over the course of the next few slides and the next uh bit of time is I'm going to go through each one of these and kind of show you what you would need to do to get it set up. Um, now that being said, [clears throat] excuse me, these two are ones that you will install. These two you would install locally on your on your own machine. And this one is um uh cloud hosted by Google and it's free. Um all of these are free but uh the first two VS code and Jupyter notebook you would install on your own machine. Collab you would just access through your web browser. It is hosted by Google. So that's an advantage. You don't really need to install anything. And for that reason um sometimes we will favor Collab. Uh and for other reasons too. Collab has some really nice features if you've never used it. Um, but notebooks, um, Jupyter Notebook and Collab are very similar. They're very similar. Collab just has its own spin off on on the notebook, um, type of file that Jupyter Notebooks work with. And it's um, like I said, kind of cloud hosted. So, I'm going to I'm going to walk us through each one of these and [clears throat] explain to you what they do, what they look like, and then we will um I'll set up each one of them uh kind of in a live demo so you guys can see. Um but uh we throughout the program, it will really be up to you which one of these you want to use. There's no hard requirement to use any one of them. It's really going to be your preference which one of these tools you want to use to work with Python. Whatever one you feel comfortable working with, that's the one you should use. All three of these are very popular in the industry. So, you're not missing out by using one versus the other. Um, they're all very popular. Even Collab, I know it wasn't on the screen, but it is widely used in in the community and the industry. Uh, no system recommendations for training LLMs. Um, no. Because we don't we won't really focus on that until the end. When we get to when we get into generative AI, we'll talk about that. When we get into generative AI, we'll talk about that. So, yeah, we're not we're not focusing on LM in the beginning. That's that's an advanced topic for us. What is my personal preference? Um, I like Visual Studio Code. Um, personally I that's what I use for my day-to-day work is uh Visual Studio Code. I like Visual Studio Code and I like Collab a lot. Um, so you know, we'll talk about this, but one of the advantages to Collab is that it has free access to GPUs, which is huge for doing things like uh neural nets. Um so we will lean on collab quite a bit later on uh later on when we um actually get to deep learning and neural nets we'll because collab has free access to GPUs. I'll show us that it's it's really nice and when you do anything with neural nets it usually benefits you to have a GPU access. Um, so that'll be nice. But I usually do most Python coding inside of VS Code. It supports Python pretty pretty well. What is more commonly used in the industry? Um, the two most popular are Visual Studio Code and and Notebooks. Jupyter notebooks. They're both like you can't go wrong with either one. Those two are really popular. Jupyter notebooks and Visual Studio Code are really popular. There's there's both of those you would be okay with. Either one. Let me start with Visual Studio Code. So, um now Visual Studio Code is a more general code editor. So it's actually you can edit lots of different languages inside of VS Code. Um so you could do Java, you could do C, you could do Scala, you can do Go, you can do all kinds of languages are supported inside of Visual Studio Code. So it's a really fantastic product for programming in general, not just Python. Um it has built-in terminal support. It has co-pilot integrated into it which is nice for AI like generative AI assistance working with your code which is nice. Um of course it supports Python which is what we are interested in. Um it has it has Python tools. I will show us which ones we should install as part of VS code so that we can work with Python files and notebooks. Um, so it's it's a really great code editor in general, which is why I like using it. Um, but in particular, it's pretty good at working with Python. It it supports Python pretty uh deeply. Um, so for that reason, VS Code is really really popular. But just keep in mind, you can actually use it for many different types of code that uh that people write. uh JavaScript um Java as I said like many languages are supported inside of Visual Studio Code. So it's a more general code editor. It happens to be really great at working with Python though. All right. So I'm going to show us a demo on setting up VS Code. Now we are going to do this for each one of these for Jupiter and for Collab. I'm going to I'm going to do similar demos. So, um, don't worry, we'll get to those, but I want to start with VS Code to show you kind of how to get that set up and what it looks like. Um, so where you can find this demo is inside of the demos that I mentioned earlier in the reference material. So, I'm going to I'm going to jump over to that. Let me show you guys. So, I'm back in the LMS. You guys will want to download the demos. I think somebody linked it earlier in case this didn't show up for you, but we're going to be inside of the demos and we're going to do demo one for lesson one. We're going to do lesson one, demo one, which is going to be the VS Code demo. So, the main steps that we're going to do is just going to be to point you to where to install Visual Studio Code. So it is a it is an application is a free application you can install on your machine. Um so uh you will want to follow this link that is within the demo file this code.vvisisualstudio.com/d download and download it for your particular platform. So if you're on Windows obviously choose the Windows. If you're on a Mac, um, choose Mac and make sure that you choose the right, one of the precautions is to choose the right Mac platform. So, if you have like an M1, two, M3, and four Mac, choose the Apple Silicon um, button. If you're on an older Mac, um, then you'll want to use the Intel chip one. Um uh if you're on if you happen to be on Linux which I don't probably most of you are not but if you are um you want to download the right uh distribution uh version but uh follow this link first that's the first step very easy step just go to that site pick your right platform and uh go ahead and download the installer and mostly we will be walking through the steps in the installer And then um I will show us what it looks like once it's installed and then show you a couple additional steps that are actually not mentioned in this file that I think are worth doing to get you set up. Uh yes, we will be doing Jupiter next. Yes, we we'll we're going to be covering VS Code, Jupiter, and Collab. I'm going to show us examples of all of those. Okay, let me ask you guys. Were you guys able to get to the download page and start that download and installation of VS Code? Able to do that? Any issues with that? Okay. Yeah, it's just like any yet. I love I love the optimism yet. Uh already having both of them installed. Okay. Yeah. No, if you already have it installed, I mean, great. I'll show So, if you if you already have VS Code installed, great. You can sit tight. I will show you um a couple of extensions that you'll want to add for Python support if you have it installed already. I'll show us how you can use it with Python in particular. If you already have it installed, perfect. Looks like you have it launched. Still working on it. Okay. So, these these instructions um uh show an example of someone that would be on a Microsoft platform um walking through the installation. Uh if you're on a Windows, you probably want to create a desktop icon. You definitely want to add it to your path. And this just shows what's being installed. So this is all the install wizard on Windows. Nothing that exciting there. So this if you follow all these steps, you will have it installed. I hope you have enough disc space. Uh I don't think it's too big. I don't think it's too too massive. I forget how much space it takes. I don't think it's that much. I don't think it's that much. But um yeah, hopefully you have enough it. So if if you don't uh if you do not have enough disc space um don't worry because we're going to do collab which doesn't require you installing anything. So you can always use that option. All right. So if if for some re let me just say that too just even if if it's not a dispace issue if you have an in any installation issues no worries because we will work with collab and Google that is going to be cloud hosted that you don't need to install anything you just need a Google account okay a free Google account um so no worries at all if you cannot get any of these things installed the which are going to be Jupiter and uh Jupiter and VS Code. Where do we go? I haven't said yet. It just I'm just making sure it's installed for folks. I'm going to I'm going to go over to it in a second. But did we generally get it installed and do you have it open? So, if you once you get it installed, uh once you get it installed, then open it. Yeah, you need to get it installed. Uh, it should be this first. It should be this link here. Follow this link to get it installed. Oops, I pasted the wrong link. Let me find I'll copy and paste the link. But yeah, take take a moment to get it open. Once you have it open, just sit tight if you want to. What does it say? Yeah, feel. So, for you guys seeing the co-pilot features, um, click click use AI features. I think that's okay. Yes. Um, you'll you'll likely want co-pilot. Yes. Click click okay on that. That's the link, by the way, for the download in case uh we needed to get to it. So, I'm going to go over to VS Code and show you what it looks like on uh my end. Okay. So, you should have something that looks roughly like this. I don't have anything open. I don't have any files open. Uh I just kind of have a blank screen here. Um, but if you I would recommend uh using the AI features if you can. Um, I think that'll come in handy later on. Um, are we comfortable uh moving forward? I want to show us the extensions that support Python. So, right now when you first when you first install this, it does not work with Python out of the box. We have to install a couple extensions inside of here to get it to work with Python. I'm going to show us how to do that. Don't worry about tuning any settings. No, don't worry about doing any of that at this stage. Don't really need to tune anything. We just need to get Python support. So you guys with me on this main page? You can use your corporate. Sure. Sure. Yeah, you can you if you have it if you have copilot and want to use your corporate, you can use that. That's fine. But you guys are with me on the main page because I'm about to show us uh I'm about to show us the extensions we need to install to work with Python. Okay, really important because this isn't this is not in the documentation. Um, no, no need to reinstall. Um, you can I'll show you how to add that through the extensions. No need to reinstall. You can add it as an extension. Yeah. So, let me ask you guys on the left, do you see this little box icon that if you hover over it says extensions? Do you see that? you. There may be other things here too, but at least that one with the extensions. Okay, so we do see that one. Okay, so what we want to do No, I wouldn't I wouldn't uninstall. That's okay because we're actually going to install Anaconda to get Jupiter. I wouldn't un I wouldn't I would cancel that if you can because you're going to want that for Jupiter as well. I wouldn't uninstall Anaconda. I wouldn't uninstall, but I mean if it's already going if it's already doing it, that's okay. We'll just reinstall it later. All right. So, back to the extensions. So, let's click on the extensions. Okay. So, do we see something like this that has a search bar for extensions? Do we see the search bar for the extensions? Okay. What do you think we're going to search for? We're going to search for Python. Yeah. So, you are going to want to install the official Python extension from Microsoft. It is this one that has the blue check mark next to Python. Uh, so there now there are other ones here, but we just want the one that says from Microsoft. Do we see that extension? When you type in Python, do we see that one? So just so it should just say Python. It should be Microsoft. Uh it's really popular. It has a lot of downloads. Over 192 million downloads as an extension. It's from Microsoft. Okay. Click on that. Click on that. And then you should see an install button. It I already have it installed. So it says uninstalled. But right here, there should be an install button. Install the Python extension. It's out of 192 million installs. Really popular extension. Are you guys able to install it? You want to install that? It should be pretty quick. It should be pretty quick. It's not that big of an extension. So, what this does is just the Python Sherry. It's just a Python one. If you go into the extensions and then search for Python, it is just the one. It's just this one that says Python and it's from Microsoft. Python blue check mark Microsoft. You want that one. And then you want to click on that one and then hit the install. Um, Roberto, is that for a co-pilot? Is that for a co-pilot? I maybe try closing it and reopening it. Try closing VS Code, reopening and retrying the install. Um, no, we're not opening any folders right now. We're not opening it. We're just installing the extension. That's all. We're just installing the extension. We're not opening any project folders. Were we were we able to install that? I know there's a lot by Microsoft, but there should just be one that that says there. So, see how the name like this name is this name here is this name is Python debugger. This one is Pilance. Just the one that says Python. Just that one. That's the one we want. Only that one right now. Okay. Perfect. Perfect. Okay. Great. Okay. So, one more extension for you guys. So, once you install that one, I have one more for you that you want to install. Are we ready for that one? One more we want to install. Okay, we're ready for the next one. So, the next one you want to install is the Jupiter extension, which is the Jupiter. It's this one. It's the very first one here on my screen. So, it's it says Jupiter and it's from Microsoft. Okay, we want to install that one. Jupiter and it's from Microsoft. Want to install that one. So, this one has 98 million uh installs. You want to install this one. Did you guys find that one? So you want to type in Jupy Ter and it should be the Jupiter extension here that is uh from Microsoft. So install that one. Great. Now what does this one do? This extension will allow you to work with Jupiter notebooks inside of VS Code if you want to. So you Jupiter notebook has its own standalone program which we will look at next. But you can open you can have those files, those Jupyter notebook files be compatible with VS Code and open them and edit them and run them inside of VS Code if you want to. So this extension gives you the flexibility to work with notebooks inside of VS Code. So you never have to leave VS Code if you want to work with notebooks. Um, so this is a good extension if you really want to work with notebooks and stay inside of VS Code. Yes. Uh when you Yeah. When you install install an extension, it might it might install a couple other dependency extensions. Yes. But that's okay. Those are required. That's okay. That's that's that's okay. All right. How do we feel? Good. Uh did we get those installed? Did we do were we able to get those installed? [clears throat] Okay, here is how we will test that it all worked. So, we're going to do something really simple. Here's how we will test that it worked. Let me go out of here and back to our files. So, out of the extensions, I just went to the top button where it's the little file um icon and um I am going to um go up to the very very top where it um so you guys see on your VS Code window where it says file, edit, selection, view. I'm just going to create um I'm just going to create uh a new new file. So, do you guys see that where where you say file edit selection view? Click on file and then click on new file. You should see what I see on this screen right here. If you see if you see Python and Jupyter Notebook then you know those are installed correctly. Do you guys see these options text Python and Jupyter Notebook? Great. So what that means is we we can now create those kind of files in the future. We can create notebooks. you can create Python files and VS Code will be able to work with those. If you don't see Python, that means your Python extension didn't install yet or you didn't install it. So, you want to go back to you want to go back to your extensions and make sure you installed So go go go to this button over here, the extensions, type in Python, and then make sure you install this Python extension. Okay. So, you're going to install the Python extension and you're going to install the Jupiter extension, which is this, and install both of those. Make sure those are installed. If they're installed and you still didn't see that when you went to file um new file, if you don't see those, then um try exiting VS Code and relaunching it. Okay? Try exiting VS Code and reopening it and seeing if you can make a new file. Okay? But it should be under uh at the top file and then new file and then you should see those options Python and Jupiter. Once you have those extension installed, you may need to close out of VS Code and reopen it to see that. Okay, perfect. after you relaunched. Okay, perfect. Yeah, you may need to relaunch so that it can show the it can show the extensions. Yeah, perfect. Okay, perfect. So, that's set up for you guys. So, um Perfect. It's set up for you guys. Uh we will work with it in the future, but just wanted to make sure it was installed and set up. Once we start working with Python, um I will show you guys how to how to work with it. Um but glad that's set up for now. Uh what issue are you having uh Romero? Is it not showing? It's not showing Python or Jupiter for you when you do file new file. It's not showing those. You may need to exit VS Code and reopen You uh Sil, yeah, you can you can make one. We're not going to do anything with it right now. It's not going to you're not going to do anything with it right now, but um it's make sure you're searching for it with a Y. It's J U P Y Ter. You have to search. You have to So when you go when you click on the extension, search for JUP Y. It should be the first thing that shows up with Jupy Ter. It's this Jupiter one from Microsoft. I kernel I'll So the let me show us let me show us that later. The kernel you have to um you have to have a Python So you may need to install a Python interpreter to to be able to run the kernel. So, I need to show us that. Um, but I I don't want to get into that Save what to Oh, wherever you want. Wherever you want on your own machine. It's up to you. It doesn't really matter. Just wherever you want. It doesn't matter. It's up to you. All right. So, what I want to do is uh I want to take a break. Um because now, you know, I said after two hours, we'll take a longer break. Um so, we will now we'll take a 10-minute break. Now, um if you're still having any issues, um we can try to get you set up at the end of class. Um but we are going to set up. So, coming up after our break, we're going to take a 10-minute break. Coming up after that, we'll we'll go and install Jupyter Notebook. And then after that, we will look at Collab. So, you're going to have multiple options to run Python. Not So, if this wasn't working for you, that's okay. We'll try a different route. Okay? I will try a different route. Um I I know Collab will work for you because that is hosted by Google and really easy to get working with. So at the worst case scenario, Collab will work for you. I know it. Um but we'll try to get Jupyter Notebooks installed for you as well. But if you're having issues with VS Code, let me know at the end of class. We'll try to get you set up, okay? You're still having issues with it. But, um, what we're going to do right now is take take a 10-minute break. So, let's try to be back um in about uh 10 minutes. Let's call it an even um let's call it an even uh what will we be covering? Um installing the other installing the other um Python setups. So, Jupyter notebook and working with collab. And then we will get into the basics of Python's the syntax. So we're going to talk about indentation, identifiers, um maybe if we have time, basic variable types, data types. Yep. So we'll get into Python. We will get into Python today. All right. So let's jump over to uh Jupiter notebooks. So um what's so special about Jupiter? Well, it turns out that uh Jupiter is a platform for running what are called notebook files. So obviously we just installed the Jupiter extension in VS Code which will allow us to run notebooks in VS Code. But Jupiter has its own notebook platform and that's what you will install in this setup. Um notebooks are special. They are um really great um Python code files that give us the ability to execute isolated what are called cells of code. So we can run one cell at a time and test and debug the execution of that single cell without affecting any of the other cells. So, um, notebooks are great for, uh, running code live and interactive. When we do a lot of our demos in this program, they're all going to be in notebooks. Um, so that we can kind of run things one cell at a time. Um, uh, no. So without notebooks you either have to run you run like a Python script like a Python file um which is a py file and usually you have to either run that through a debugger or run the entire script at once. You don't really get code isolated into individual cells which is really nice with notebooks. The other thing is notebooks are easily sharable. So you can share a notebook with somebody else and they can open it and see all of your inputs and outputs in the notebook which is really nice. Like all of the outputs get saved into the notebook. Um which is nice. So and notebooks uh especially in the Jupiter platform are going to have all the data science libraries available to them. So, uh, if you're people usually love doing notebooks for working with data, um, really easy to work with data inside of notebooks and and build things like plots. You can display your, uh, you can display your graphs really easily inside of the notebook and then share your notebook so other people can see your graphs. Um, so notebooks are really awesome like interactive environments for running code. Um and we will favor notebooks uh as our primary way of running code throughout the program. Now where you open those notebooks is up to you. You can open them in VS Code. You can open them in the Jupyter notebook platform. Uh you can open them inside of Collab and run notebooks in Collab. Uh notebooks are very very popular. Why isn't running in notebooks the default? It's because uh not all code runs inside of cells. Like applications are not going to be well suited for notebooks. Like Instagram is not running in a notebook. Uh it's more structured into actual Python files and actual uh more structured programs are going to be not in a notebook. Notebook is more for prototyping and debugging and uh executing small chunks of code to test it out. It's not for writing larger programs like an like a an LLM application like a chatbot would generally be in not in a notebook. It'd be in like a Python file. Uh cells versus class objects. So cells are just small uh think of them as small little environments to execute our code. Um class objects are actual chunks of code that define an object. They're they're different things. Yeah, different things. We'll we'll learn about objects. Um and we will certainly see what cells are as we go through. I'm going to show you an example of a cell coming up when we install Jupiter. But uh let's talk about let's uh go through the installation of Jupyter notebook so you can see what a notebook looks like. I think that'll be helpful to orient. So let's go over to that demo. So this is going to be demo two uh demo two inside of um uh lesson one. So, we're going to go over to that. Everybody has this one. Okay, perfect. Okay, so you're going to follow this instruction. Now, what this is going to do is first No, this has not this is not going to be anything to do with VS Code. This is going to be a different platform. This is going to be Jupiter. Where is this? This is the This is the demos. This is uh demo two inside of that demos folder that we said uh to to uh grab all the demos from your LMS. Does anybody have the uh demo 2 PDF they can upload? I I think somebody uploaded all of them earlier, but if you have demo two, want to upload it real quick? I don't have the PDFs if somebody wants to share that. So there so they're different. Um VS So what I was saying is you can open notebooks inside of VS Code and the thing that allows you to open notebooks in VS Code is the extension. So yes, if you're going to work with notebooks in VS Code, you need the extension installed, but you can use the standalone Jupiter platform to work with notebooks. It's up to you. If you like using VS Code, um if you like using VS Code, you can do it that way. If you like uh the Jupiter platform, you can do it that way. It's up to you. It's just a preference. I'm giving you guys options. That's my goal is to give you options and let you guys choose what you're most comfortable with. Okay? And we're and we're taking time to do that now in the beginning of the program, right? Because we're going to be doing a lot of Python examples coming up as we start learning Python. So, it's it's valuable to spend that time now. I know it can seem a little slow, but I promise it'll be worth it so that you guys have options for running your running your code. Yes, thank you guys for uploading those. Appreciate it. Those are the demos you want to uh follow along with. Okay, so the first step here is going to be to install Anaconda. Now, you may be wondering, what is Anaconda? I thought we were talking about Jupiter. And that's a valid question. Anaconda is a what's called a distribution of Python. So Anaconda is a program a software a collection of software programs that give you a version of Python with a bunch of packages uh with a bunch of packages already installed. Um and then uh one of those is the Jupiter package so that you can run Jupyter notebooks. And what Jupyter notebooks will be is a uh basically a web browser application that will open up a notebook editor in your web browser. So that's ultimately what we're going to do, but we are going to install it via the Anaconda distribution uh via the Anaconda distribution of So that's where we're going to start is with the initial download of Anaconda. Oh, it's no no skipped registration. Okay, let me let me uh open the link. I think there I think there's a way to find it without having to do the registration. There's a way to get to it without having to do that. I'm going to find it real quick. Oh, you can't. Okay. So, if you can't install it, that's okay. We will be able to work with notebooks in Collab. It will and you can work with notebooks inside of VS Code. That's fine, too. Yeah, I'm getting I I'm going through the registration process so I can um I can show you that install. Okay, let me share my screen. Did you guys get to once you go through the like setting up your account, do you get to this page? Do you get to this page for those of you going through? Yeah, that looks right for you, Ashish. That looks right. Do you guys get to this page though when you get through your like account setup? Okay, you got to this page. Okay, so then choose your correct Windows or Mac down. You want to be over here on the left. You want to do Anaconda distribution. This is what you want to do. So, choose the right one. And if you're on an M1, M2, M3, you're going to do the Silicon. If you're on an older Mac, you're going to do the 64. And then obviously if you're on a Windows, you should be clicking over here to do Windows. But you want to do the Anaconda distribution, not Manonda. Okay. So click on the installer for Anaconda Okay. And then let that install. Now while that's installing let me explain something about the difference between uh I think it was asked earlier what's the difference between um Anaconda uh as the default Python. So Anaconda as I was saying earlier is a version of Python that has a bunch of data science and machine learning packages already installed for you. So uh it comes with a bunch of packages that are already installed. So if you use that Python um that Python has a bunch of packages built in with it that you don't need to go out and install. So Anaconda is a very popular version of Python for people to install that are working in data science, AI, ML very popular version because…

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