Hello everyone, welcome to this video in this video I am going to give you an overview about this Python AI and machine learning suite for production and development what this is what this course will cover and you know how to use this to learn Python based AI machine. Okay, so folks, before we start you know there are multiple ways you can learn a new technology one one is proactively wherein you need to know you can get some book or you know, some tutorials and do it yourself right from the scratch. The other ways you can go to some training instructor, instructor based or virtual training and follow it and learn it that way. Each one has its pros and cons. If you are doing it on your own, then you know sometimes you get stuck. There are no setups you need to install and whatnot and you don't use sprint A lot of time in unnecessary activities.

So, that's the con, if you go for the instruct instructor based or virtual training, then you know there is a cost involved in that. Now, what we are trying to achieve through this course is to give you middle path wherein, you know, we capitalize on you know, something which is already done to learn machine learning and AI using Python, which is basically a book very popular book for learning AI and machine learning. So, you already have, you know, the way to learn the things okay. But in addition to having the book, you need to do the setup and all those things. So what we are trying to do over here is provide you the setup out of the box and then you can use the book and Then learn it yourself. So it's a middle path wherein, you know, it's a kind of training as well as it's a kind of, you know, safe learning.

Okay. So this is what this Python based AI and machine learning suite is for. Okay? So what what that book is what is that book on which this whole course is based on. So that book is from this author, you can go to this GitHub page or better you can go to our website, you can go to for products, click on the products and baton AI machine learning suite. So this is our product page.

It has all the details about this product. And over here you can see this is based on this book from this author It's a very famous book, you can click on this one, and it will take you to the GitHub repository by the author. This is the official repository by the author. And here, this is the book by machine learning. And here is the Amazon page and bacteriophage, okay, so you can, if you are not aware about the book, you can, you can go through it and see the reviews and all those things, you know, it's four and a half stars. So pretty, pretty popular book.

Now, this book, and the author has made a lot of hands on code available. So you can see in the repository, all this code. So what this product AI Machine Learning Kit this product does is it gives you all those code, the setup required to run that code everything out of the box. Okay? And on top of that, so So this kit This course will cover first of all having a pre install setup, how to procure that or provision that setup, that setup comes with all the modules, the Python modules required for running the code which are there in this in this machine learning kit, machine learning book, okay. So, so the setup will have all the required installation, it will have all the demos already cloned from the get repository along with that, it has a Jupiter hub environment, okay.

So what is a Jupiter, Jupiter or ipython what is a Jupiter hub? This we will discuss it in subsequent videos. But just to give you an overview Jupiter basically allows you to access your Python based environment via browser. Okay, so you can running your code in a browser rather than running it from command from. So, you know, you can run your setup in a cloud environment and access that from browser from anywhere over the internet. And what Jupiter hub does is Jupiter, so Jupiter, only Jupiter, it comes with only a single user environment, okay, but if you have a requirement, for example, if you are a training institute where you have multiple students who are trying to learn or if you are a school or education institute, or you know, company as well, where you want to have a single setup accessible to multiple users, then you can make use of Jupiter hub.

So what Jupiter hub does is it it gives you a separate environment for each users. So let's say user a will have on Jupiter or ipython environment, user B will have its own ipython or Jupiter environment and so on. So, Jupiter hub and Jupiter ipython environment is there and there are other goodies like you know you can access the virtual machine or the setup through Remote Desktop there is this fish fishes friendlier and interactive shell. So, this is another thing you will get and then there is Visual Studio code if you want to do your development in an ID instead of doing it in ipython notebooks. So, all this so, this is all about the Python a machine learning suite for production and development. So, you can use it for your production environment as well as for your development environment.

Now, how you get this one So, as I mentioned before, through this, we are giving you middle path To learn AI and machine learning, using Python based on a very famous book, which has all the quotes and covers a lot of topics here in the demos you can see these are the topics which are covered by the book with with the working demos, so how you get it. So So this was an overview of this course and what this AI Machine Learning Kit is all about. Now let's see how to, you know, get into the setup and how to accept that. Okay, so what we have done is we have created the setup, and made that setup available on Google Cloud, as well as AWS. Okay, we have separate videos, you can go to our support page and in our support page, you will Get all the steps, all the videos and all the instruction on how to provision the virtual machine which has the set up and everything for for this course.

Okay, so on the support page, you can go to Python Yeah, machine learning kit. And you know, you can follow the guide over there and you will get the details. Okay. All right. So so the product page, gives you details about the product, the support page will have all the video tutorials and other instruction on how to utilize it. Okay, so, so that's that that about the product and the support page.

Now, there are separate videos, how to provision the VM on a zoo, the Google Cloud and AWS, you can look into those videos in our support page, and follow those instruction and provision the virtual machine. Okay, now in this overview video, I will To show you how that virtual machine looks like, I already have the VM up and running. There are three different ways you can connect to to the VM. One is through the shell. So if you are, for example, on Google Cloud, so for for this video, I'm using Google Cloud where my VM is provisioned, you can connect it through the command prompt. The other way is through ipython environment.

And the third way is Remote Desktop. So let's start with the remote desktop. I already have the VM running. So when you follow the instructions on each of these, for each of these cloud providers, and you will know how to connect to the remote desktop. So in the subsequent videos, you follow the instruction, get connected, preserve the VM, get it get connected to the VM through remote desktop, so I'm connected to the VM in this VM. You will see we got the Visual Studio code this visual Studio code can be used as an ID for your development purpose.

So this is Visual Studio code. Next one is at the desktop. We have this Python Machine Learning Kit. If you open that one, you will see this folder. In this folder, you have this code folder and all the code sample codes, which you I showed you, from the author on the GitHub repository is already cloned over here. Okay, so you can navigate it from here.

You can open it in Visual Studio code and, you know, if you want to do any tweaking, you can do that. If you want to create new one. You can do that. Then this whole setup is based on Anaconda. If you're not familiar, I highly recommend you Google about Anaconda Python Anaconda. So this setup is the machine learning environment is created using Anaconda and that Anaconda setup is under slash home.

So, if you go to File System home directory you have this Anaconda folder. So, here you see all the setup for the virtual for the world for the AI and machine learning environment okay all the packages required to run your code from the book are already installed at this location okay. And if you are familiar with the Anaconda environment, what it provides you is virtual environment for you know, you can create separate Python virtual environment. So, for for for this one, the virtual environment where all the libraries are installed is called PI ML. Okay, so how you can Navigate to that one you can open a terminal what you can do is you can get this finally TC nifty Jupiter hub. This is the daemon which will be running in background for ipython which we'll see in in a short while.

So, when I execute this source command what it will do is it will activate the PI ML environment this PI ML environment has all the setup for a machine learning conda list it will list all the packages. So, this is the setup and also appeared appear this and also as I mentioned all the setups in the PI ML virtual environment okay. If you are not aware about Anaconda or what is a virtual environment and those things, don't worry about it. This is just to give you some idea about how the setup is installed. This will be transparent when you are ready To learn the AI and machine learning topics, okay? This is just to give you some background.

Now, this is the desktop environment, you can connect to the VM and you know, you can use it, okay? But the more convenient way is to use the Jupiter hub or ipython. Okay, so in this ipython environment, how you connect is, once you have your VM provisioned, you will have the IP address for your VM. Instead of doing a remote desktop and accessing the VM which might be slow. The connectivity might be slow at times so your remote desktop might be slow. So the other way is you can just copy your IP address, hit enter and it will take you to the Jupiter environment for Wired username and password and it will provision an iPad notebook for you for Ubuntu user.

Right? So then it will directly take you to the virtual machine. home directory where we had the AI and machine learning folder where I showed you that all the demos from the GitHub are copied. So you don't need to access the remote desktop, you can use the public IP, use the browser and directly login. And here you have your, the book, the code from the book and other artifacts. Okay, so this is how you're going to use the virtual machine.

Next is what is there in this in this folder. So this folder consists of this very important README file, so you can click on that. So this README file will open another window and this README file explains you, what are the different topics which are covered in this book. And here you can see table of contents and all the notebooks. So this notebooks then you can run it from the virtual machine. Okay.

So the idea we're here is to give you an out of the box environment to learn a Python, Python bestie and machine learning by using this book. Okay, so this README file at the top at the top of the directory will give you the table of contents, give you an overview about the book. And then each of each of the demos or each of the topics in the book, okay, like there are around 16 topics covered in the book. Thank you for Have those topics you have demos. So those demos are available under this folder called code. So you can see from chapter 01 to chapter 16, you have the code.

And for each of the code, demos or chapter demos have its own README file. and here also at the demo level, you have one README file, so you can click on that README file, and then it will explain you about the demos. Okay, so the top level README file, let me just navigate back to the top level README file over here. This README file is about the book. The other README file inside the code, which is this is about the sample code or the demo code. Okay.

And then within each of the chapters, there is a code and then there is a readme file. So for chapter 01 Okay, Chapter 01 is talking about machine learning giving computers the abilities, you know, getting started chapter. Okay, so this chapter one. If I click on the readme file within chapter one, it will open up in another browser window, and it will explain you what this chapter is all about. Installing packages, all these things. Now for you, lucky for you, all these things are already done in this VM.

So you don't need to worry about setting up the environment. So you can skip this chapter one altogether. Okay. Then you can navigate back to your code, click on chapter two, click on the readme file for chapter two. And this README file for chapter two explains the demo or the code for chapter two. So what that chapter two covers all the text and everything is over here.

And then you can click on this. So by the way, I, this extension I by MB is an extension for IPython notebook. So this README file itself is a notebook. And the code for this chapter two is in this 02 IPython notebook. Okay, so you can click on this, and then you can run the code, how to run the code and execute the code. We'll cover it in our next video.

Okay. All right. One more thing I wanted to tell you is this README file itself is a IPython notebook. You can double click, and it will, the formatting will change sometimes, you know, by mistake you, you might double click on that and the formatting will change. So all you need to do is just refresh the page. And then the actual formatting readable formatting will come.

Okay, so just a note around how to use the readme files. Okay, so just to summarize, we have this product called 500 Machine Learning Kit, this product is available. The this product basically is a combination of a virtual machine, which are available on a zoo, Google Cloud and AWS and a set of videos like this video, which helps you learn the AI and machine learning using the Python environment, and as per the book mentioned, already are displayed over here. Okay, so I hope this course will be beneficial for you and He will learn something out of it. So thanks a lot for watching.