AI Agents Full Course 2026 | AI Agents Tutorial for Beginners | How to Build AI Agents | Simplilearn

Key Takeaways

Hey everyone, welcome to our agents full course by simpler. Are you curious about AI agents and how they're changing the world right now? Then this video is just for you. AI agents are everywhere and in huge demand. Learning how they work could open doors to amazing jobs in AI, automation, and tech. In this full course, we will start with the basics. What AI agents are, how they surroundings and how large language models work, power them behind the scenes. We'll also dive into what generative AI agents really do. Cover the full road map and even go over some math basics like neural that will help you understand about machine learning. We've also included tutorials on key machine learning like GANs, NLP, K and confusion matrix explained in easy way. But before we commence, if you're interested to master the future of technology, then the professional certificate course in generative AI and machine learning is the perfect opportunity for you. Offered in collaboration with the ICT Academy IT Kpur, this 11-month live and interactive program provides hands-on expertise in cutting like machine learning tools like chargu and even hugging face. You gain practical experience through 15 plus projects, integrated labs and master classes delivered by esteemed IT carpool faculty. So hurry up and enroll now and find the course link in the description box below learns YouTube channel. In this tutorial, we will learn about AI agents. Before that, let's take a quick look at what exactly they are. As AI continues to evolve, there are no longer just chat bots responding to your commands. It's about intelligent systems that can understand objectives, make decisions, and take actions. AI agents are autonomous systems that perceive their environment, process information, and take actions to achieve specific goals. They use machine learning, natural language processing and reasoning techniques to make decisions. AI agents can be responding to stimuli. They are widely used in virtual assistants, automation, and decision-m systems. Advanced AI agents like conversational AI and autonomous agents can adapt and learn from interactions. That said, if these are the type of videos you'd like to watch, then hit that like and subscribe buttons and the bell icon to get notified. Also for your information, if you want to upskill yourself, master generative AI and artificial intelligence skills and land your dream job or grow in your career, then you must explore simply learn code of various generative AI courses and certifications. Simply learn offers various certification programs in collaboration with some of the world's leading universities like Purdue, IIT, KPUR, Guati and many more. Through our courses, you will gain knowledge and work ready expertise in skills like advanced Python, machine learning, generative AI, and over a dozen others. That's not all. You also get the opportunity to work on multiple projects, learn from industry experts working in top tier data and product companies and also academicans from top universities. After completing these courses, thousands of learners have transitioned into an AI and machine learning role as a fresher or moved onto a higher paying job and profile. If you are passionate about making your career in this field, then make sure to check out the link in the pin comment and description box below to find a generative AI program that fits your experience and areas of interest. Now, without further delay, let's get started. Firstly, let's have a brief discussion about the agenda for today's session. So these are the following points that we will be discussing in the AI tutorial for beginners. Firstly, we will get started by understanding what exactly is an AI agent. Next, we will learn the critical differences between AI agents and large language models. Followed by that, we will understand how exactly an AI agent works. Then we will have a brief overview on the future and risks of AI agents. Followed by that, we will have some final thoughts on AI agents. and then we will switch to a live hands-on demonstration on how to build AI agents. I hope I made myself clear with the agenda. Now let's get started. Firstly, what exactly is an AI agent? So at its core, an AI agent is a software program designed to perform tasks autonomously. Unlike traditional software that strictly follows predefined rules, AI agents can make decisions based on understanding, interaction, and realtime data. They leverage cutting edged AI models like GPD, Claude, Gemini to analyze, process, and execute tasks dynamically. Think of it as a digital assistant that doesn't just follow rigid instructions. Like try saying check if Tony is available on Friday and book a meeting. This is a usual approach, right? Instead of following a routine approach where you try to say to your digital assistant this way. Check if Tony is available on Friday and book a meeting. This is a routine approach where you say to your digital assistant to check the availability of toning and book a meeting which is quite straightforward and simple to understand. But in case if you take it to a much complex version but if you just change a couple of terms and make it a little more complex like try saying schedule a meeting with Tony at the earliest convenient time in the next month. Right? It might seem a little similar to the previous one, but here the AI agent will have to follow some steps like this one. Check the calendar for available slots. The AI agent will log into the recipient's account which is Tony's account and checks if there are available slots for the meeting. Then it will determine the ideal time followed by that it will send out invitation which happens all autonomously. In the previous case, directly the digital assistant will check for a Friday in the calendar and books a meeting for the time that you have provided regardless if availability is there or not. This is the difference between a digital assistant and an AI agent. Now let's move ahead into the next topic of today's session which is about how are AI agents different from LLMs. While AI agents use large language models like GPD4, they go beyond simple text generation. Here are the key differences. LLMs generate responses based on trained data but do not actively interact with the real world. Whereas on the other hand, AI agents can access external data, plan tasks, and execute actions all in real time. This is one of the critical defenses between LLMs and AI agents. Now for example, if you ask LLM about a sports match that happened yesterday, it may not have an updated knowledge. While on the other hand, AI agents can fetch live data from the web databases or APIs to provide an accurate answer. This is how they behave and perform. Also, there is something called hallucinations. You might be wondering if this term is related to humans. Yes, it is related to humans. But for a change LLM can also hinate. For example, if you ask unrealistic question, let's say there are different types of apples, Fuji, Macintosh, Black Oxford, right? If I write tell me about watermelon apple or orange mango which are unrealistic or in a way different fruits chances are there where an LLM can give you some false or unrealistic information that may sound believable. This is termed as hallucination and of course recent LLMs have a scale to measure their hallucinations as well. Now let's shift to the next point of today's discussion which is about how do AI agents work. Now AI agents function as a sophisticated problem solvers with four essential capabilities. Let's start with planning then interacting with tools then memory and knowledge access and lastly executing the actions. Now let's go through each one of these steps in a detailed way. Firstly planning. AI agents begin with a goal. Whether it's researching market trends or drafting an email, they break the goal into actionable steps following a chain of thought process to optimize the execution. This removes the need for predefined human triggers. The next stage is interacting with the tools. Unlike basic AI models, AI agents can interact with tools, browse the internet, query databases, and use APIs. This extends their capabilities beyond static knowledge. Now the third one is about memory and knowledge access. AI agents can retrieve and store information using techniques like R A G or also known as the retrieval augmented generation. This allows them to integrate company specific data, market research or customer support queries to generate up-to-date responses. Now the last and important step, execution of actions. Beyond just processing information, AI agents can take actions, writing reports, managing applications, or even communicating with other AI agents. This makes them powerful automation tools capable of independently handling complex workflows. Now, let's move to the next part, which is about the future and risks of using AI agents. AI agents represent a new era in automation and efficiency. However, their autonomous nature comes with risks. Without proper safeguards, an AI agent could misinterpret objectives. For example, if instructed to achieve world peace, an unregulated AI might arrive at extreme solutions. Ensuring human oversight, ethical programming, and responsible deployment will be crucial to leveraging AI agents safely and efficiently. Now let's go through the next point where we will have some final thoughts before getting started with the live demonstration. So coming to the final thoughts, AI agents are redefining how we work and interact with technology where their ability to plan, interact with tools, store knowledge and execute tasks. They are far more advanced than traditional LLMs. Now that we have discussed the theory part of today's session, let's move ahead to the next part where we will have some hands-on demonstration on how to build AI agents. So today we will be building an AI agent that can help you browse through your internet. Now we will be using browser use because of its highest accuracy based upon its competitors. So it has about 89% compared to other competitors. Right? So there are three different plans open source, pro and enterprise. So for now we will be going with the open source version which is readily available on GitHub. So here you'll have the complete repository. So this is the browser use. Here we have an official page of browser use where we have the connection to quick start and all the steps are arranged in learning order. You can just follow the steps and get ready with the environment for building your AI agent. So we have opened up the command prompt. First we need to check Python if it is available or not. So it's one of the prerequisites. So Python version we have about 3.9. So uh according to the prerequisites we need to have anything above 3.10. Not a problem. Let's download Python. So here just go to Windows. I'm working on Windows. Windows 3.13. It should take a couple of moments. And the playright is also one of the prerequisites and UV is also one of the prerequisites. It's a Python package which helps us to create a virtual environment. So if you just follow these steps, you can also create a virtual environment for yourself to run your AI agents. Everything is just follow the steps. Right. Let's have a quick look on where exactly we are on the Python installation part. There you go. Python has been successfully downloaded. You just have to run the execution file. And make sure you click on that add python.exe to path so that you don't have to manually create the uh you know environment variable for that. It'll automatically create and everything is as simple as that. Just set up follow the default methods of setting up Python in your local Windows operating system and it should be done. Just give it some time. There you go. We are shortly closing the first step which is adding Python to your Windows operating system with the latest version. There you go. The installation was successful. Now let's start command prompt and check if we have successfully updated Python version or not. Simple. The same command Python version. Enter. There you go. So we have 3.13. Now rest of the part will happen on the command prompt. So let's expand it and also have the page open for browser use so that we can proceed with steps and install everything. So we'll go to the GitHub page. So before going to the GitHub page, let's go to the official web page where we need to follow some steps to prepare the environment first. So here we have uh a command to set up an environment for UV. Okay, UV is not internal. So let's try to go through uh stack overflow to resolve this issue. I think uh we might have to install UV first or let's give it another try. Let's run the same command. Okay, the same issue. So I think we have to update uh UV first. So let's copy paste uh that particular code from UV website. I'll show the So I think we have to set the UV environment first. So we have an official documentation here which shows some steps to be followed to set up UV. So just copy the code from the official website and run these on the command prompt as mentioned here. Just enter. So the first package has been successfully downloaded and yeah everything is installed. Now let's go back to the official web page and try to execute the second command in the row. So that's the one request a specific version by including the URL. So for a specific version we need to copy it and then we have the pip install uv and lastly we can proceed with the uh you know uh environment here python. So here's the uh next one. I think we might have copied the wrong code. So, let's copy the right code here. I paste it there. Execute the second code as planned. Downloading. It might take a little time for unboxing. Expansion in progress. There you go. Everything is installed. Now, we can get started with setting up the environment for Python and AI agents. Go to the quick start for browser use. Oh, we missed one thing. We need to pip install UV first. Yeah, there you go. Okay, I think we are facing an issue again. Let's go with uh the Windows version. I think this should work. Yeah, collecting and downloading the metadata. It might take a little while. Once we install UV, then we can continue with V, which is virtual environment for Python and our AI agent. uh we are not having that in here. Let's go to the browser user visual page. So yeah, I guess we are all set. I think uh we can get started with executing the virtual environment. So let's go to the quick start web page. So copy the code. Just type it down there and hit enter. That's the one. So we have 3.13. Uh will it execute? Let's give it a try though. Yeah, it is executing. I think it took a little time. There you go. Now we need to activate the virtual environment. For that you can see activate with vb scripts activate, right? You just need to copy paste that code. Don't forget the dot. It's not copying. Don't worry. We can just manually type it down if it doesn't copy. Or you can also go to the web page. Actually, it is also mentioned in the steps. You can just copy it from the web page and paste it here or you can choose to type it down. So we have successfully in the virtual environment. So you can see the username is present inside the brackets. So that means you are in the virtual environment. Now you can proceed with the further steps of setting up the environment before you build an AI agent. Now we are back on the browser use. So here that was the step that we need to take. Now copy the browser use code which is mentioned here to download the dependencies. Enter. So packages are getting prepared to unbox. I think shortly the download will be finished for browser use. There you go. We're good to go now. Now let's proceed with the next command. So the next command is about installing playrite. Just copy the code and the next part will be about creating your AI agenda. So playright is the last dependency that you need to install. Again it might take a little while. Let's wait. There you go. Playright has been successfully installed. Now the last and final step is getting started with building the AI agent. Now talking about the AI agents, you can go to browser use and go to some examples from their repository or you can choose to copy this code from browser use official documentation and customize your AI agent. So here if we go back to browser use repository go to examples here you already have a lot of examples or AI agents built by browser use. So you can click into use cases and you will find a lot of them. So identifying capture shopping or you know checking out Wikipedia pages for information scrolling and searching using the search bar to search for a specific keyword right all of those are readily available in browse website or repository you can give it a try and learn things from that. So you can do this by cloning the repository. For that just click on the code and here copy this code extension and run it on your window. It is running in virtual environment. So it can be cloned by using g space clone space the command. Okay. G is not recognized as an internal or external command. I think we are supposed to install git as well. For that let's try to open a new command prompt and try to install git in that. So there you go. We have a complete detailed procedure to install and run git in the Windows operating system. So these are the steps to be followed. Let's go to Git official website and download Git first. So I'll go with the 64-bit version for my system. Yeah, it's downloaded. So, basic installation procedures following the default steps. There you go. Finish. Now, go to a new Windows command prompt and check for the version. Okay, it's not still recognized because I think we might have to set up the environment variables for this. Unlike Python, Git doesn't provide an automatic path. So, you can do that by selecting the environment variables. Not for the account. Yeah, not for the account. We might have to go for the system variables here. Let's go back. Yeah, system variables. Create new. You can add the variable name as. Okay. Let's cross check one last time. Cancel. Yeah, you had to go into system variables and create new. paste the path from git right so for that we might want to go into the program folders and then git folder and cmd that's the location I'll just name name it as git and this is the file path let's use this lowest case okay done now let's close the earlier command prompt and uh yeah it's not working so let's close and restart the command prompt as provided here verify. Close and reopen your command prompt or PowerShell. Okay, let me start a new command prompt. This is a virtual environment. Let's not mess with it. So, get version. So, we have successfully installed git. And now let's try to clone the repository in the virtual environment. I think we might have to restart the Yes, there is a requirement where you have to restart the virtual environment as well. Not a big problem. Let's try to do that. So before we restart uh the virtual environment, let's open a new command prompt and try to clone it there and then replicate the same on the virtual environment. So this is the new uh command prompt. Let's copy the code and we will use the get clone keyword. So it is getting cloned. So it's working. So now we can retry this on the virtual uh environment. So I'll close the entire command prompts. So there you go. In the virtual environment the file already exists. So we have successfully cloned that file. Now we will go back to desktop and create a new folder empty folder and rename it as AI agent right so far we have used and uh set up all the environments required by browser use now to create our AI agent we also need an interface for that browser use I have already provided a web user interface web UI that's the second link right so we will be downloading and installing that into this particular file so uh let's start the command prompt again and go back to desktop. So you need to just provide cd desktop. So this will change the directory to desktop and here on the desktop provide the location of AI agent. So I have user AI agent. You will be inside this folder now. Yes, there you go. Now you will need to execute some commands where you will install web UI or browser UI into this folder. Go to the homepage browser use web UI. This is the link GitHub link and here the same procedure you will be provided with stepbystep guidance to install web UI and you'll be ready as soon as you do it. So we'll be cloning it. So we have already installed uh get clone. So get clone space code run to clone all the repository into this AI agent folder. There you go. It's done. For cross verification, you can open the web UI folder in desktop and you'll have all the dependencies. Now let's continue with the next steps. So in the next step, we will change directory to the web UI folder. We'll be inside the web UI folder now. There you go. So whatever the next steps that we will be executing will be inside the web UI folder. So they say we have to enter into the virtual environment again. So UV. So I think uh they recommend having UV. So we have already installed UV. But anyways for safer side let's try to execute this command prompt. So we have two options Mac Linux Windows. So we'll go with Windows. Select the code. Paste it here. Done. It should take a couple of moments. So as the second procedure, let's copy the second code segment as well. And uh let's go back to the command prompt and see what's the status. And there you can see already everything has been installed. Let's also do the second step as well. So we've pasted the second code segment and it's unpacking. So let's paste the third snippet which is about creating the virtual environment. So for that we will be installing UV as we have done previously. Yeah. So let's go back to the web UI interface and u copy the steps provided here. So we will be creating a virtual environment using this particular code snippet which is uv Python 3. Erase the previous code. Enter. and we will be shortly inside the virtual environment. Let's activate when V. So if you're going through Windows, you just have to select scripts. And if you're using Linux and Mac OS, there's a different statement. So we'll be using with scripts. There you go. We are inside a virtual environment. And here the username is web UI, right? So according to the next steps we need to install some dependencies. For that we need to use uip install our requirements txt. Just copy paste the code. There you go. The dependencies are getting downloaded. It might take a little while. There you go. Uh the dependencies might get downloaded and unpacked shortly. Just give it a little time. I guess it's done. Now let's go with the next procedure where we will have to install playright. Once again, go back to the uh GitHub page. So the code has been copied to install playbrite. And next we will have uh docker. I think we'll go with local setup. Right? So if you are transferring the AI agent to a different user and making the agent public then you can go with the docker application. But if you're creating an AI agent for yourself and your purpose only then you can go with a local setup where you can go with the local setup and it will be active only in your PC in local uh environment. So uh this will be your port player right has been successfully installed I guess. So uh here you are creating a user interface for yourself and once you execute this command the command prompt will give you an IP address. So it will be in the form of uh local host and you just have to copy paste that particular URL in your browser. Just wait for a little while while it gives you the URL. There you can see running local on URL. So you need to copy this URL http col/12700.1 double quotes 78 as your port number. Copy that and enter on any of your local browsers. So I'll open a new uh window over the browser and I'll paste it over here. There you go. Now you can see a web UI is successfully running with an interface for your agent. Now your agent is not completely done yet. You can see you have some maximum run steps and maximum actions per step. If you're providing a command, it it runs a certain number of steps, right? You can can customize. You can either increase or decrease, right? Those are a few things you can choose to customize. So I'll take the maximum actions per second to 15. Now you can see the LLM settings. This is the most important part. By default we have open AI here. So I'd like to go with Google and inside that we have the model 2.0 flash. And uh now we need something called as an API key which can be created. So uh you just have to go to Google studio for that. Let's open a new tab, Google Studio. Select the first link. You might have to sign in here. So, don't worry about the pricing. This is opensource, free of cost. Let's quickly sign in. So, now I've successfully signed in. So, here you can just create uh get API key. So, you're using Google AI studio here. So we'll try Gemini and get API key. Can close this prompt here and accept all the terms and conditions. Now just click on get API. Scroll down create API and now you will have your API created successfully. Let me remind you this API will be only for your personal use. So don't share it with anyone. Just copy your API key and you can see even uh the windows says don't share with anyone. So copy the API key and go back to your browser and paste the API key right there. Now you can choose to run your agent here. You can type down your steps to follow for your agent. So let me type in go to google.com and search for simplylearn.com or simplylearn website. So if you go to the command prompt here you can see stepbystep execution. So it has started a new uh window for Google browser and you can see it is in signed out mode. Clearly it's accessing the search prompt and I'm not typing anything. It's doing everything by itself. simply.com. Okay, it's selecting the enter button or search button by itself. There you go. It has identified the search results and now it should identify the page as well which I think it is already in progress. Yeah, it has already started the Simply Learns homepage. There you go. And you can also find a course that you are looking for as well. Great. Now let's take a step further. Let's stop this application here and let's try to uh you know take a step further and try to provide an email address and um even the login credentials and ask the AI agent to use that particular credential login and send out an email. Since we have already seen this particular step has been executed in signed out mode, right? So let us provide a command for our AI agent with the credentials and such a way that it can open a new window and can log into the Google Gmail account and send out an email. Right? Let's try that. So I'll erase the previous prompt. Go to google.com. Enter step two. login to Gmail and let's provide the email address along with the password. Now let's state compose an email to uh let's also provide a random email address here as well. Subject will be enrolled to AI and ML program. Let's try to run the agent now. I see it has opened a new window. But uh okay, let's check if it selects the sign in option. Yeah, it is selecting the sign-in option. It has recognized it. Okay, it opened the sign in page as well. It is trying to type in. If I'm not wrong, due to some or the other security restrictions, it should not be able to perform it. But still I can see uh the cursor highlighting in the window. Let's not lose hope. Let's see what it can do. No, it stopped automatically. So that means u it does have some limitations but it's anyways good. So far it's so good. So in case if you wanted to uh you know kind of book some credentials or book some items on the online website or search for the best uh items with low cost some uh preverified commands which are not security concerned then so far so good this particular agent is very good for you and if you are taking a step beyond where it's ethically not logical to perform then the agent is choosing to withdraw itself from that command. Picture this. You've got a virtual assistant. Let's call it your smart AI buddy. So, this assistant doesn't just follow basic commands like setting reminders or sending emails. This AI can think for itself, make decisions based on the situation, and even learn from every interaction it has. It can predict what you might need, solve complex problems, and even adapt to new challenges without any input from you. That's the magic of agentic AI and it's not thing of the future, it's happening right now and the exciting part is you can build it too. So in today's video, we will be taking a deep dive into the road map to learn agentic AI step by step. So here's what we will cover. First of all, we'll start with the basics of programming focusing on the most beginner friendly language which is Python. Then we look into the machine learning and natural language processing to teach your AI to learn and understand human language. The next step is deep learning and transformers, the powerful models behind most modern AI systems. Then we'll get hands-on with generative AI where we will learn how to build AI that creates text, images, and many more. We'll also see how agentic AI works, how you can create intelligent agents that can interact with the world and make decisions on their own. and we'll wrap it up on how to deploy your AI agents on the cloud to make them live and ready for real world use. So by the end of this video, you'll exactly know how to create and deploy your own AI agents. So let's dive in and start this exciting journey together. So let's start with the very first step which is learning Python. Now before we can build intelligent AI agents, we need to learn how to communicate with our computer and that's where Python is the language we will use to do that. Python is one of the most easiest programming languages to learn and it is widely used in the AI development. It's very simple, easy to read and most importantly it has got huge community of developers which makes learning and troubleshooting much more easier. Python also allows you to focus on problem solving rather than worrying about the complex syntax. But what exactly is programming? At its core, programming is just writing a set of instructions to tell a computer what to do. Think of it like teaching your robot how to follow a recipe. You provide the step and it executes them. Now, for agentic AI, you won't just need to know the basic Python. You will need to get comfortable with some specific libraries and concepts that are most relevant to AI development. Now these would include libraries from data manipulation, machine learning and natural language processing NLP. So let's dive into some of these. Now we'll just check the libraries and use cases relevant to Agentic AI. So the very first library you will encounter is NumPy which will help you to handle and perform calculations on large data sets. Agentic AI often deals with massive amounts of data and NumPy is great for making these calculations faster and much more easier. Next, we've got pandas. Think of pandas as a superpowered Excel for Python. So, it will help you to organize, clean, and even analyze data very efficiently. Now, since agentic AI requires handling lots of data to make decisions and learn from its environment, pandas is a must know. Then we've got Matt plot lib and seaborn for data visualization. Now while you might not immediately think of graph when building an AI agent, understanding data trends visually is a huge part of training and refining your model. Now for actual AI development, the key libraries are scikitlearn and tensorflow. Now, scikitlearn is great for machine learning projects where your AI needs to learn patterns from data. TensorFlow on the other hand is a powerful tool for building neural networks which is exactly what you will use for deep learning models like those that power agentic AI. Finally, we've got NLTK and Spacey which are popular libraries for natural language processing. Now since agentic AI needs to interact with humans and understand language, these libraries also help your AI process text, understand sentences and even generate humanlike responses. Now where should you start learning Python? Now if you are a complete beginner, Simple Learn offers a great Python for beginners course that walks you through the basics and builds up the more advanced topics including machine learning and many more. So if you want a link, I'll just mention the link in the description box below. So you can just directly check it. Now let's move on and talk about some key Python concepts for agentic AI. Now to get started with agentic AI, here are the key parts of Python you will need to focus on. First we've got variables and data types. You would need to understand how to store and manipulate data. Then we've got loops and conditionals. These are very important where you will need your AI to make decisions on repeat task just like a robot performing a task continuously. Then we've got functions and classes. Now functions will help break down task where classes will allow you define your own AI object and behaviors like defining an intelligent agent. Then we've got libraries and modules. So Python's power come from its libraries. You would need to learn how to import and use them effectively. For agentic AI, you need to focus heavily on understanding data structures such as list and dictionaries because you will be processing a lot of data. You also need to get comfortable with functions, classes to organize your AI behavior and the abilities into neat reusable pieces. So now that you know how to communicate with computer using Python, it's time to teach our AI agent two major technologies which is machine learning and natural language processing. So, what exactly is machine learning? Machine learning is a type of artificial intelligence that allows computers to learn patterns from data and improve over time, all without having to be told every single step. Think of it like this. Imagine you want to teach a robot how to recognize if a review is positive or negative. Instead of telling it exactly what to look for, you will give it a lot of example positive and negative reviews. and then it starts to learn from them. The more examples you give, the better it gets at recognizing them on its own. That's what called machine learning. And here's how it works in Python. Let's say we want a robot to learn whether a review is positive or negative. So based on some example reviews, we feed it to the data and let the model figure out the rest. It's just like teaching it to read between the lines and identify emotions in the text. And this is how exactly agentic AI works. So, Agentic AI will use the machine learning to learn from the data and make decisions whether it's making recommendation, answering a question or even automating task. Machine learning is at the heart of it all. By teaching the AI to recognize pattern, it becomes very important and smarter over time, better at making decisions on its own. Next, let's talk about the natural language processing. Now that our AI agent can understand and interact with us in human language, NLP is the technology that allows computers to read, understand, and even generate text just like a person. Think of it as your Siri or Alexa that can understand what you're seeing and respond back to you. That's NLP in action. For example, let's say you ask Alexa, "What's the weather like today?" So the NLP will help Alexa understand that you're asking for weather information and then it responds accordingly. Now with the help of agentic AI, NLP is very important because AI needs to understand conversation commands and questions from humans in natural everyday language. So whether it's answering customer queries,uling appointments or even writing emails, NLP allows your AI agents to interact with the world in a very meaningful way. For example, you can use these libraries to build an AI agent that can read a customer review and tell whether it's positive or negative, just like we did in our machine learning example. Or you can even create an AI that responds to customer inquiries by understanding their questions and generating appropriate responses. Now that we have covered the basics of machine learning, let's take things up a notch with deep learning, a more advanced form of machine learning. So deep learning powers some of the most impressive AI applications you see today just like self-driving cars, virtual assistant like Siri and Alexa and even AI generated text. At the heart of deep learning is something called neural networks. Think of neural networks as a mini brain for your AI. They're designed to process data in a way that mimics how the human brain works. What makes them so powerful is that they have multiple layers that allow them to recognize patterns in data much more accurately than traditional machine learning models. Here's an example. Imagine you are teaching an AI to recognize whether a picture is of a cat or a dog. Now, with the help of neural network, the AI doesn't just look at the whole picture at once. It breaks down the picture into smaller parts starting from simple features like the edges and the colors and then building up to more complex features like the ears or fur. Each layer of the neural networks adds another level of the understanding and that's how deep learning work and that's why it's so good at task like recognizing images, translating languages and even generating creative content. Let's say we have an AI that can identify objects in images like self-driving car. When the car's AI see an obstacle, it needs to quickly recognize whether it's a pedestrian, a vehicle, or something else. Now, using deep learning, the AI can process this information accurately and in real time, allowing it to react quickly and prevent accidents. But wait, there's more. Let's talk about transformers. A specific way of deep learning model that has completely revolutionized the world of AI. Transformers are gamechanging because they're incredibly good at handling sequences of data like text or speech. For example, language translation or text generation. These are the things transformers excel at. So transformers are behind some of the most powerful AI models you have probably heard of like GPT3, the model behind chat GBD and Google Bird. These models can understand and generate humanlike text with remarkable accuracy. Now let's break it down for you. Imagine you want to create a chatbot that can have a real conversation. The challenge is that conversations don't happen in a straight line. People talk in paragraph, ask questions, reference past comments. So there's a lot of context to keep track of. Transformer solve this problem by looking at the entire sequence of words, not just one word at a time and understanding the relationship between them. This allows the model to grasp the full context of a conversation, just like how humans communicate. Here's an example. If you were to give a transformer a prompt like once upon a time there was a young prince, it could generate the rest of the story in a coherent engaging way just like a human author might do. Now let's talk about transformers in agentic AI. Let's connect all of this to agentic AI. Now since agentic AI is all about creating intelligent agents that can interact with humans, transformers play a huge role in making these interactions natural and meaningful. So whether it's like having a chat with a virtual assistant or having an AI solve a problem through text, transformers are what allows these agents to understand complex language and generate responses that make sense. So after you have understood the deep learning and transformer models, let's move on. And at this point, we are ready to get hands-on with generative AI, a type of AI that creates something new like text, images, or even music. So in this part we will explore how AI generates humanlike text, images and even music all on its own. Frameworks like land chain and langraph are very important and perfect for building geni models. Now let's talk about something really exciting which is agentic AI. So this is where things get truly magical because agentic AI is a special type of AI that goes beyond just following instructions. So you would need to understand what exactly is agentic AI and how AI is different from the other AI systems that has the ability to make decisions, interact with environment and learn on its own. For example, an agentic AI could be used to schedule appointments. It could look at your calendar, check availability and automatically book a meeting without needing to be told every single time. Now agentic AI can even manage task across different platforms. Imagine it handling emails, setting reminders, and organizing your work all at once. It's a really powerful tool for businesses and individuals looking to save time and automate task. Now, to build agentic AI, you will need to use frameworks like feed data, crew AI, and even land chain. Now, these frameworks make it easier to create AI agents that can think, learn, and make decisions on its own. So once you've built your AI agent, the next step is to deploy it in the cloud so that it can scale and work on the user globally. Cloud platforms like AWS, which is Amazon Web Services and Google Cloud, provide powerful tools to host and run your AI models in a way that ensures they can handle large amounts of data and even traffic. So when you deploy an AI agent on a cloud, it's not just running on one computer. It's running on powerful servers that can handle multiple users at the same time globally. And why is it important? Imagine you have built an AI agent to handle customer service question. Now, if you only want to run it on your personal computer, it won't be able to handle lots of customers all at once. But when you deploy it on the cloud, you can make sure it scales up automatically, handling thousands or even millions of users without crashing or slowing down. Most of you are already familiar with Alexa, Siri, and Google Assistant. Some of you are used to smart devices like an air conditioning system that can sense your absence in the room and automatically turn it off. Have you ever wondered who these are or how do they do all this and how can they be so human? Don't worry, this video will help you learn everything you need to know about these artificially intelligent devices also known as AI agent. Agent is a freestanding program or entity that interacts with its surroundings by sensing them with sensors acting with actuators orectors and so on. Coming to software agents, this agent acts on sensory inputs such as file contents, key strokes and network packets I repeat and network packets it has received by acting on those inputs and showing the results on a screen. Coming to human agent, we are all agents of goods. Humans contain sensors like their eyes, hearing and other organs as well as actuators like their hands, legs, mouths and other bodily parts. And third one is robotic agents. Robotic agent feature a variety of highquality motors that serve as actuators as well as cameras and infrared range finders that serve as sensors. This is all about examples of AI agents. Agents must do to certain rules. Let us list them out. Rule number one, an AI agent needs to have an environmental perception. Rule number two, decisions must be based on observations of the environment. Rule number three, action should follow decisions. And coming to rule number four, the AI agent's action have to be logical. The best possible outcome comes from making rational decisions that maximize performance. Now that we all know the rules to follow, we will understand types of agents. First is learning agents. A learning agent learns from its past experience. It is used in the gaming industry as this industry needs the most reliable and tested agent. Next is reflex agent. These agent focus on the now and forget the past. They answer by applying the evident condition action principle. When a user starts an event, the agent refers to a set of pre-programmed criteria and rules leading to pre-programmed result. You might have heard about tic tac to and the decision regarding that based on reflexive agent. Model based agent. These agents select their behaviors in a similar manner as reflex agent but they have a more thorough understanding of their surroundings. The internal system has an environmental model that integrates with the agent's past. Next is goal based agent. These agent expand on the data that a model based agent maintains by adding goal information or data describing desired outcomes and circumstances. Next is utility based agent. They are similar to goal based agents but they also provide an additional utility metric. This evaluation ranks each potential consequence in relation to the desired outcome and chooses the curse of action that optimizes the result. Examples of rating criteria includes factors like success probability or the quantity of resources needed. Now we know the types of agents. It's time to know its function. So let us learn about structure of an agent. Many agents uses the PAS model in the structure. P is an acronym for performance measure environment actuators and sensors. For instance, let us take vacuum cleaner as an example. Performance, cleanliness and efficiency can be considered as performance. Environment rug, hardwood floor, living room, these can be considered as environment for vacuum cleaner. Actuator brushes, wheel and vacuum bag in a vacuum cleaner can be considered as actuators. Sensors, dirt detect sensor, bump sensor. Next we will see how to improve the performance. We only need to question ourselves. How do we increase our performance in a task? When confronting the problem of how to improve intelligent agent performances. Clearly the solution is straightforward. We carry out the task, recall the outcomes and make adjustments in light of our memories of prior price. The same manner that humans improve, so do AI agents. By saving its previous responses and attempts, the agent improves and learns how to react more effectively in the future. Artificial intelligence and machine learning convers. If you ever wondered how machine learning can now understand and generate humanlike text, you are in the right place. From chatboards like Chat GP to AI assistant that powers search engines, LLMs are transforming how we interact with technology. One of the most exciting advancement in this space is Google's Gemini or OpenAI RGBT. A cutting edge large language model designed to push the boundaries of what AI can achieve. In this video, we will explore what LLMs are, how they work, and why models like Gemini are critical for the future of AI. Google Gemini is part of a new wave of AI models that are smarter, faster, and more efficient. It is designed to understand context better, offer more accurate responses, and integrate deeply into service like Google search and Google Assistant, providing more humanlike interactions. So, we will break down the science behind LLMs, including their massive training data set, transformer architecture, and how models like Gemini use deep learning innovation to change industries. Plus, we will compare Google Gemini to other popular LMS such as OpenAI chat duty models, showing how each of these technologies is used to power chat bots, virtual assistants, and other AIdriven application. By end of this video, you will have a clear understanding of how large language models like Gemini work, their key features, and what they mean for their future AI. Don't forget to like, subscribe, and hit the bell icon to never miss any update from Simply Learn. So, what are the large language models? Large language models like charge GPD4 generative pre-trained transformer 4 o and Google gemini are sophisticated AI system designed to comprehend and generate humanlike text. These models are built using deep learning techniques and are trained on vast data set collected from the internet. They leverage self attention mechanism to analyze relationship between words or tokens allowing them to capture context and produce coherent relevant responses. LLMs have significant application including powering virtual assistant, chatboards, content creation, language translation and supporting research and decision making. Their ability to generate fluent and contextually appropriate text has advanced natural language processing and improved human computer interaction. So now let's see what are large language model used for. Large language models are utilized in scenarios with limited or no domain specific data available for training. These scenarios include both few short and zero short training approaches which rely on the model's strong inductive bias and its capability to derive meaningful representation from a small amount of data or even no data at all. So now let's see how are large language model train language models typically undergo pre-training on a board. All encompassing data set that shares statical similarities with the data set specific to the target task. The objective of pre-training is to enable the model to require highlevel feature that can later be applied during the fine-tuning phase for a specific task. So there are some training processes of LLM which involves several steps. The first one is text prep-processing. The textual data is transformed into a numerical representation that the LLM model can effectively process. Thi