AI Complete Crash Course for Beginners
Learn AI & ML Fundamentals from Scratch
Introduction to Artificial Intelligence
Welcome to our AI crash course! In today, we're going to cover the core fundamental concepts of Artificial Intelligence (AI).
When we unlock our phones with Face ID and ask Siri to tell us the weather, we're actually using computer vision and NLP (Natural Language Processing). When we interact with tools like ChatGPT or Gemini, we're using LLMs (Large Language Models).
Today, every application is full of recommendations. If we use Amazon or Flipkart, they recommend what to buy. If we use Netflix or YouTube, they recommend what to watch. We have apps like Google Maps and Uber that do traffic prediction for us. They also do arrival time estimation with great accuracy.
Even when we're coding today, we take help from tools like GitHub Copilot. So if we're using technology today, then we're actually using different multiple forms of Artificial Intelligence every single day.
Today's session has no prerequisites. Anyone who wants to understand AI, anyone who wants to explore more about Artificial Intelligence can learn from this lecture.
After today's lecture, we're actually going to feel more confident and we're actually going to understand more about the terms that are related to AI, such as Machine Learning, Reinforcement Learning, Computer Vision, NLP, Neural Networks, and many more algorithms like these.
What Exactly is AI (Artificial Intelligence)?
AI is basically that technology that allows computers and systems to perform tasks that typically require human intelligence.
For example, if we have any computers, if we have any systems that perform such tasks that require some level of human intelligence, they perform those tasks with the help of this technology which is AI.
How does AI work? Examples:
Pattern Recognition
We as humans are very good at recognizing patterns in data. For example, let's suppose someone gave us data that for input 1, output should be 1; for input 2, output should be 4; for input 3, output should be 9; for input 4, output should be 16. In this data, we'll see a pattern. And by analyzing that pattern, we'll know that if someone gives input 5, then output should be 25 because all these numbers are squares of these numbers.
Speech Recognition
We as humans, if we understand each other's language, we can understand what the other person is saying. We can understand its meaning. We can understand the context, what emotions are in it. If we want machines to perform this type of speech recognition, we can do that with the help of AI.
Today we have Siri, we have Alexa. In fact, with tools like ChatGPT and Gemini, we have voice options. So we can interact with these tools with our voice. How do these tools recognize exactly what we're speaking? This is happening because of speech recognition.
Image Analysis
We can look at images and analyze what different objects are in the image. If there are numbers written in the image, what are those numbers? What does each thing in the image mean? We as humans can do this using human intelligence.
For example, if someone gives us a car image, in that car image we can recognize where the number plate is and what numbers are written on that number plate. If we want computers to do this work, we'll do it with the help of AI. This is a practical example that actually exists.
In traffic departments, such systems exist that can detect any vehicle's number plate and send automated challans to those vehicles. These are all examples of tasks that require some level of human intelligence, and we can perform these tasks with the help of AI.
Machine Learning - A Branch of AI
If this is AI, then the most important subdomain of AI is Machine Learning. The majority of AI that we see today is actually Machine Learning.
What is Machine Learning?
Machine Learning are basically those algorithms that learn from data rather than programming. In Machine Learning, data becomes very important. In fact, that is why in the last one to two decades, Machine Learning has become more important.
Many such systems have emerged that actually use Machine Learning because as the internet came, as all people came onboard the internet, we started getting a lot of data. Today, every big company has a lot of data, and that is why we're able to see so many practical applications of Machine Learning algorithms.
Relationship Between AI and Machine Learning
Since Machine Learning is a subdomain of AI, we can say that all Machine Learning is AI, but not all AI is Machine Learning. There are some parts of AI which are not Machine Learning.
Examples of AI that are not Machine Learning:
- Rule-Based Systems: These are part of AI. They have some level of intelligence but they're not part of Machine Learning. In them, we do programming rather than learning from data.
- Classical Robotics: Where we do rule-based programming. That is also AI but not part of Machine Learning.
- Algorithms like A*: Used in graphs which is part of AI but not part of Machine Learning.
- Fuzzy Logic Systems: Used in our home ACs, inside fridges to program them, to give them some level of intelligence. That is part of AI but not part of Machine Learning.
But the majority of AI we see today in LLMs, in Google Maps-like, Uber-like, Amazon-like, Flipkart-like apps, the majority of that AI is Machine Learning.
In Machine Learning, we have many different algorithms that we'll look at later, such as Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, and many more.
Deep Learning - Subdomain of Machine Learning
Deep Learning is very popular today. Deep Learning is basically that subdomain of Machine Learning where we work with Neural Networks.
We can create Neural Networks through machines, and these networks are actually inspired from the human brain. The human brain has neurons, and inspired by them, we have Neural Networks.
From Neural Networks, many different algorithms emerge, such as:
- FNN - Feed Forward Neural Networks
- RNN - Recurrent Neural Networks
- CNN - Convolutional Neural Networks
- Transformers - Used in tools like ChatGPT today
All of these are part of Deep Learning.
Generative AI - Application of Deep Learning
Generative AI is at the center of this, which means Generative AI. In Generative AI, we deal with those technologies, those systems that work to generate new text, new audio, new video, new images.
Until now, all the AI we were dealing with weren't generating much new content. But when we start dealing with such AI, such technologies, such algorithms that generate new content, then we're talking about the field of Generative AI.
How Machine Learning Works
In ML, we use algorithms that learn from data. Let's take a very simple example.
Bank Loan Application Example
Suppose we work in a bank and many people apply for loan applications. As a banker, we need to create a system that can predict whether any applicant should get the loan or not.
We can design this system in two steps:
Step 1: Training
First, we look at old data to see how many applicants have applied for the loan so far. We figure out different characteristics of those applicants. We analyze the data and see which applicants' loans were accepted and which were rejected.
When we analyze this data, we'll see some common patterns, some common characteristics. For example, credit score is something we'll see - what exactly is the value of credit score that makes loan accept or reject. Similarly, we might see some pattern based on salary, education level, collateral, etc.
This way we'll find multiple characteristics through which we can form some logic in our mind about whether to reject or accept the loan for any applicant.
This is the first step that Machine Learning algorithms also do, which is called Training. Training basically means learning from data.
Step 2: Making Predictions (Inference)
Once we've trained in our mind, formed our logic, then comes our second step. If any new applicant comes to us now, we'll analyze that applicant's parameters and check whether the loan should be accepted (Yes) or not (No).
This way we're able to make a prediction. The first step was Training and the second step is actually called Making Predictions, or this step is also called Inference.
Inference means that we're using the logic we found from training, the model we built about whose loan should be accepted, whose should be rejected. We're using that model to make the predictions.
These two steps are what majority Machine Learning models follow. Meaning, Machine Learning models first train on their past data in the first step, and then once they train, we have a model prepared in a way. The model is the logic we prepared after analyzing the past data.
In the second step, whatever new data comes, new input comes, based on that we make predictions which is also called Inference.
Other Examples of Machine Learning Applications:
- Medical Field: Working with X-rays to detect if cancer exists or not
- Gmail: Automatically detects if emails are spam or not spam
- Credit Card Fraud Detection
- Swiggy/Zomato: Applications that estimate delivery time
Summary: Machine Learning is the process of teaching computers to learn patterns from data (this is Training) and make decisions based on those patterns (this is Inference).
How Machine Learning Differs from Traditional Computer Science
In traditional Computer Science, generally any program takes some input, and inside that program some logic is written, and based on that logic it produces some output.
How do Machine Learning algorithms work? In Machine Learning, the algorithms take both input and output. The data we send to Machine Learning algorithms has both input and output.
For example, if we take our bank loan application example, we'll have to give all applicants' data which is basically the input, and for all that data we'll have to tell if the loan was approved or not, which is basically output.
So the data we'll send here will have both input and output. Every Machine Learning algorithm doesn't take both input and output parameters. There are different types in Machine Learning which we'll study later.
But this is a simple example using which we can understand the basic sense that any Machine Learning algorithm takes both input and output, and based on that it produces logic. And this logic is actually what the Machine Learning model is.
It basically analyzes this data about which input produced which output, and that same logic we call our model. And once we have this logic, we feed our new input to this logic, then based on that it gives us output predictions.
So this is how a normal Machine Learning algorithm is different from a traditional Computer Science algorithm.
Basically, whatever Machine Learning algorithms we're talking about, they take any data as input and they produce a model for us. Such a model that can now make predictions for new data, can produce output for new data.
Types of Machine Learning
Machine Learning primarily has three main types:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
1. Supervised Learning
Supervised Learning is basically when our models learn from labeled data.
What is Labeled Data?
For example, take our spam detection system. If we work inside Gmail and we need to train Gmail or Yahoo to detect spam, then for that we'll have to train it on some data. That data will look something like this:
In this training data, we'll have email - what exactly email came. With that we'll have sender email. We'll see fake website, promotion, sales - such keywords when spam comes. We'll see if there's any external link? Generally spam emails have a link with them. How many exclamation marks are there? This could also be an example of how we detect spam because spam emails are generally very unprofessional.
Based on all these attributes, we'll decide if our email is going to be spam or not. So we'll give our email a label if it's spam or not spam.
This type of data we call labeled data. Labeled data is basically that data where both input and output exist properly, and they are in a well-structured manner.
Here, this entire input we have, we call this our input data in training, and this is represented by X. And this label we have is basically our output which is represented by Y. And in input, these different columns we have, in data we call them our Features.
So in Supervised Learning, we basically try to predict what characteristics in input X, what properties exist based on which we can predict our Y.
In mathematical terms, if we want to write in simplest terms, we basically want to predict our Y, our output, on the basis of some behaviors of X. So we can say in simple mathematical terms: Y = f(X) where X is the input, Y is the output.
And here we want to tell how Y is a function of X, and this function, this logic, is exactly what our Machine Learning algorithm is going to generate after training.
So this function is actually stored inside our model which does all the predictions. Because once we understand the behavior, once we understand what function to apply on X that Y's value comes, then we can apply that same function on any new input too. So we'll have value of new output, new prediction.
Types of Problems in Supervised Learning:
A. Classification Problems
Classification problems are those where based on the input, we map our input to predefined categories and classes.
For example, we have emails. Depending upon whether email is spam or not, we can divide it into two categories: spam and not spam. This is an example of classification.
So whenever we have some fixed number, some finite number of categories in output, classes, we call it classification.
Another example would be our loan approval. We know any applicant will either have loan approved (Yes) or loan not approved (No). So there are predefined classes that can exist in output.
It's not like output can be anything, value can be anything. Like if we see travel time on Google Maps, travel time value can be anything depending upon location we entered. But for loan applicant, answer can only be Yes or No. Not infinite number of values that value can be anything.
Similarly, let's suppose we're doing image classification. If we have many images and all images have only two categories: cats and dogs. Then either our image can be a cat image or our image can be a dog image. So we have finite number of classes available for us.
Types of Classification:
- Binary Classification: When output can have only two values. Either it can be Yes and No. Either it can be spam or not spam. Either it can be cat and dog. This is called binary classification.
- Multi-Class Classification: When output has multiple more than two categories.
Example of Multi-Class Classification: Sentiment Analysis. For example, we do sentiment analysis on our text where we try to find out the emotion. Someone wrote a sentence "This is a bad day." This is a sentence. If we're building an ML model that divides any sentence into three categories based on sentiment: that sentiment is positive, sentiment is negative, or sentiment is neutral.
So we know for this sentence, the sentiment would be negative. For someone positive could be, for someone neutral could be. So how many categories can come in output? Only three categories can come in output. Either sentiment will be positive, negative, or neutral. So since we have more than two classes, this is also an example of multi-class classification.
Another example would be a very popular example in multi-class classification. Let's suppose we want to build such a system, such an ML model that whatever we write by hand, let's suppose I wrote seven here. So it detects and tells which digit this is. To build this model, what data will we use? We can use this type of handwritten digits dataset which has many pictures of digits from zero to nine.
Our model will train on this data. After that it will look at this new picture and will be able to detect which class this is. So based on this data, how many digits do we have? We have 10 digits. So our output can also be what? 10 different categories. So we can say we have 10 different classes for this particular example. So this also became example of multi-class classification where we have 10 categories as output.
B. Regression Problems
Regressions are problems where at the end we basically try to predict a numerical output. Here there are no categories in which we have to divide data. Here there is a numerical output that we try to predict.
For example, let's suppose we have Zomato, Swiggy, Flipkart-like applications where our delivery time is predicted. Now delivery time is a numerical value and its value can be anything depending upon location. If location is nearby, time could be 9 minutes, time could be 15 minutes, 20 minutes away, it could be 43 minutes, 50 minutes, 1 hour.
Here the models being used, there we're basically trying to predict a time, a numerical value at the end. And how are we able to do this? We're able to do this with the help of past deliveries data. Past deliveries, riders data we have, and based on that our predictions happen.
So this is an example of regression problem. With that we also have stock price forecasting. We also have property price forecasting. So these all also analyze their old data and based on that some price value, some numerical value is what algorithm predicts.
So in these problems, we also basically try to predict the relationship between the input and the output. Now here what is our input, we call it our independent variable. And what is our output, we call it our dependent variable. And independent and dependent variable, we try to predict the relationship between these two.
So this relationship will also be a function. So we can also say Y = f(X).
Mathematical Understanding of Regression
Let's suppose we take a simple example. We have data of many people's height and weight. Height is the input and weight is the output we're taking.
So in input we have a single feature that exists, which is the height of the person. And based on this height, we have some data that we need to analyze what weight should be at which height.
So this some people's height-weight data is already given to us which is the input data, and based on this we'll train our ML model.
Now in regression, we know that dependent variable will be some function of the independent variable. So Y = f(X) will have some relation. And the simplest relation for this entire particular data could be what? That Y = aX + b. This is one of the simplest relations we can predict.
Which means in our weight and height graph, we'll draw a single line. We'll draw such a line that goes through majority data points. And if it's going through majority data points, meaning majority data points that are there, they'll align on this line or near it.
So what is this line? This line is basically our function. It represents the relationship between the input and the output values. So this is one of the simplest relationships we can predict which is a straight line. And straight line's equation is Y = aX + b where a is going to be the slope of the line, meaning at what angle our line is, and B is going to be the intercept - where this line of ours is touching on X axis? This point is what we call B.
So mathematically now if we have any new input coming which could be X dash, then for that we'll be able to produce new output based on this particular function value which would be Y dash.
This way our regression problems work that we produce our function, then we apply that function on new input and generate our new prediction or new output.
2. Unsupervised Learning
In Unsupervised Learning, our models learn from unlabeled data. Meaning we get raw data in a way in which we try to recognize patterns. And based on the features present in the data, we divide this data into clusters.
Clusters means that we try to group related data. For example, here all these dot points that are there, they're aligning on one side, so we can group this data. These dots are aligning on one side, we can group this data.
This way groups we call clusters. And this is how we try to find patterns in our data.
Practical Example of Unsupervised Learning
Let's suppose we have many news articles. We have data of all news articles online. Now we can try to categorize this data. We can divide these articles into different categories.
For example, all tech-related articles we have, we'll try to put them in separate group. All politics-related articles we have, we'll try to put them in separate group. All sports-related articles we have, we'll try to put them in separate group.
So in Unsupervised Learning, we don't have fixed categories because we have unlabeled data. But still, by recognizing patterns, we try to organize our data into clusters or groups and create some categories inside it.
Outlier Detection
When we try to form these clusters from our data, there might be some data points which are not part of any cluster. For example, if we look at this data point, then this is going a little separate from majority group. Or if we look at this data point, this is also going a little separate from majority group.
This type of points in our data, this point and may be this point, these points we call outliers. We can call them outliers or we can call them anomaly.
So from Unsupervised Learning, we can also perform outlier detection or anomaly detection. Which is very useful in the field of finance, in medical field, and even in cybersecurity field.
For example, our cybersecurity team is analyzing some website. Now on that website, many users are logging in. But we detect for a particular user that this user under 1 minute, some particular user has logged in from five different cities. So what is this? This is something which is different from normal data. So this is anomaly which our system can detect.
Types of Problems in Unsupervised Learning:
A. Clustering Problems
Clustering problems are basically those problems where in our data we try to create some clusters or some groups.
For example, this is our unlabeled data. But inside it we've created some different-different groups. And all data points we've divided into multiple clusters. So different colors in this graph represent different clusters.
Example we've already seen that we have multiple news articles. So we can divide them into different clusters or different categories. Finance became, tech became, sports became, politics became. We can divide the data into multiple clusters.
Types of Clustering:
- Partitional Clustering: Any data point that exists in our data can belong to any one cluster. If we talk about news articles, then news article will either be sports category or finance category or politics category - it can be of only one category. Meaning all our data points are available in different-different clusters.
- Hierarchical Clustering: Single data point can belong to multiple clusters. For example, we have a news article about taxation on Bitcoin in India. So this becomes political article too. So this can come in politics cluster too. With that it can come in finance cluster too, and with that it can come in tech cluster too. So in this type of problems, single data point can belong to multiple clusters.
B. Association Problems
In association problems, we try to find relationships between different entities. And a quite popular example of this is Market Basket Analysis.
For example, Amazon-like or any other e-commerce platform. These websites have data of how users shop? Which items they buy? So by analyzing that data, these websites can analyze that with one type of item, which other item is purchased together.
For example, in any store, many customers if they're purchasing bread, then with bread those customers also prefer to purchase milk along with it. Now this is a practical example of market basket analysis and this is something that a lot of websites like Amazon and Flipkart used to suggest to users what other items they should buy.
So on Amazon, sometimes we've seen "items bought together" suggestion comes that you're buying this item. Generally these things and are what users buy. So that is an association problem under Unsupervised Learning.
3. Reinforcement Learning
Reinforcement Learning is very similar to training our dog. We need to train our dog that when we say "sit" to it, it sits down.
If any of us has a pet, we know. They listen to us, so we give them some treats. We give them some rewards. In the same way, in our Reinforcement Learning too, we train our model.
So in Reinforcement Learning, we have the Agent which is basically the model that learns to make decisions by interacting with an Environment. We have an agent that interacts with the environment.
Meaning this agent makes some predictions, and depending upon whether prediction is correct or wrong, it gets rewards or penalties for its actions. Actions are basically predictions.
Getting rewards means we'll feed our agent, our model, some positive value. Getting penalties means we'll feed our agent negative value.
So in Reinforcement Learning, agent's purpose, final purpose, is not to make correct predictions. Agent's purpose is to maximize the rewards that it is receiving. And rewards will come when correct predictions happen.
So generally in Reinforcement Learning, it's not necessary that there will be a single prediction. In Reinforcement Learning, we do a set of predictions and maximize our output.
Reinforcement Learning is used a lot inside games. Like we need to play chess, we need to play Go, then to play chess, a single move is not enough. We'll have to make many multiple moves. We might have to sacrifice our small player in between. But the purpose and goal of the agent will be to maximize the reward.
Okay, in between one-two penalties can happen. But overall we'll win the game when our rewards maximize. So our agent's goal is to maximize the rewards by making correct predictions.
How Reinforcement Learning Works Exactly?
We have an agent that basically takes actions, meaning makes predictions. So the agent interacts with the environment. And depending upon the action, if that action is correct, then in that case we get reward which is the positive value. But if action is wrong, then in that case instead of reward, we get penalty.
Simple Example of Reinforcement Learning
Let's suppose we need to create such a model that wins us the Snake and Ladders game. So to win this game, we can use Reinforcement Learning.
So let's suppose in the game, we encountered snake. Meaning some number, some dice roll happened, some number produced, snake encountered, then there we'll get penalty. Because that is not a good thing for the player. But whenever ladder encountered, then there we'll get reward.
Eventually in the game, we want to climb as many ladders as possible and reach the endpoint. So basically the objective is to win the game.
Now, practically, this particular game in real life is quite luck-based. But like chess or Go, these types of games that are there, they are skill-based or they are pattern-based. So there to win the game, we can use Reinforcement Learning.
Apart from this, Reinforcement Learning also has many practical applications. Like it is used in self-driving cars. It is also used in robotics. How robots move, walk, how they pick objects, there Reinforcement Learning is used a lot.
Tools for Implementing Machine Learning
To implement these different algorithms, there are multiple tools that we need to use.
Programming Languages
First, we need to know programming language. Python is one of the most popular programming languages used to write our Machine Learning algorithms, train algorithms on data, and make predictions. R is also another popular language. But in majority cases, you'll see Python being used in industry and in academics too.
Development Environment
We have a tool called Jupyter Notebook. In Jupyter Notebook, we write our different Python code.
Libraries and Modules
We have multiple libraries and modules that we use in ML:
- NumPy and Pandas - for data preprocessing
- Seaborn and Matplotlib - for data visualization
- Libraries like Scikit-learn and XGBoost - to train our Machine Learning models
With the help of these different tools, we implement all the algorithms we've talked about so far.
Deep Learning Concepts
Deep Learning is the subset of Machine Learning where we study all about Neural Networks.
All the Machine Learning we've done so far, we call it Statistical Machine Learning. Statistical Machine Learning algorithms we generally apply on our structured data. Structured data means whatever tabular data we have in table form.
All the data we've seen so far was almost all tabular data. So on this data, Statistical Machine Learning performs very well. But whenever we have unstructured data, there our Statistical Machine Learning algorithms don't perform that well.
In case of unstructured data, Deep Learning algorithms perform much better than Statistical Machine Learning. Because in those cases, in Statistical Machine Learning, we have to do a lot of human intervention.
What Exactly is Unstructured Data?
Examples of unstructured data:
- We have many camera recordings or we have many video recordings
- We have many images data
- We have many audio files
- Or if we have many chat messages of some application which are not in fixed format
These are all examples of unstructured data on which our Deep Learning algorithms perform much better.
Example: Image Analysis
For example, if we have any model, let's suppose in that model we want to feed many images. And these are all human faces. So we can take an example of such an image which has a face.
Now if we feed this type of image to any normal Statistical Machine Learning model, then there model's work will be to analyze this image. Now to analyze this image, model should know what exactly are eyes? What exactly is a nose? What exactly is a mouth?
This type of things we'll have to define in terms of features to our model. And that is something which can be difficult. For example, how do we tell a Machine Learning model as a human, how to program inside it that this is an eye? Do we tell it that whatever round shape on face is that is an eye? But that is not completely accurate because mouth can also be round. In fact, the entire shape of face can also be round.
So unstructured data's features, programming them in any model can be quite difficult. So here our Deep Learning models become very useful. Because Deep Learning models specialize in extracting information from raw data. They very easily extract whatever useful information is from this type of data.
How Do Deep Learning Models Work?
They basically use Neural Networks. Neural Networks are networks which are inspired from the human brain. Our human brain has neurons, and inside our brain there are networks of neurons.
In the same way, when we create artificial networks, then in Computer Science we call them Neural Networks.
Types of Neural Networks
1. Feed Forward Neural Networks (FNN)
Feed Forward Neural Networks are the simplest type of Neural Networks. In these networks, data flows in only one direction - from input to output.
They are used for simple classification and regression problems. For example, if we have tabular data, we can use Feed Forward Neural Networks on that too.
2. Convolutional Neural Networks (CNN)
Convolutional Neural Networks are very popular in the field of Computer Vision. Computer Vision means that field where we work with images and videos.
So CNNs are used for image classification, object detection, and image segmentation. For example, if we want to detect what objects are in an image, we can use CNNs.
3. Recurrent Neural Networks (RNN)
Recurrent Neural Networks are used for sequential data. Sequential data means that data which has some sequence, some order.
For example, time series data. If we have stock market data, then that data has sequence. Because stock market data of today depends on yesterday's data, and yesterday's data depends on day before yesterday's data.
Similarly, text data is also sequential data. Because in text, the meaning of any word depends on the words that came before it. For example, if we have a sentence "I am going to the bank." Now here the word "bank" can mean a financial institution or it can mean the side of a river. To understand this, we need to see the context, meaning the words that came before it.
So for this type of sequential data, RNNs are used. But today, RNNs are not used that much. Instead of RNNs, we use Transformers which we'll study next.
4. Transformers
Transformers are the most popular Neural Networks today. All the LLMs (Large Language Models) that we use today like ChatGPT, Gemini, Claude - all of these are based on Transformers architecture.
Transformers are also used for sequential data. But they are much more powerful than RNNs. They can handle much longer sequences and they are more efficient in training.
Transformers use something called "attention mechanism" which allows them to focus on different parts of the input sequence when producing output.
Computer Vision
Computer Vision is that field of AI where we try to make computers understand and interpret visual information from the world.
Computer Vision basically means that we're trying to give eyes to computers. We're trying to make computers see and understand the world like humans do.
Applications of Computer Vision:
- Face Recognition: Used in our phones for Face ID, in social media for tagging people
- Object Detection: Used in self-driving cars to detect other cars, pedestrians, traffic signs
- Medical Imaging: Used to analyze X-rays, MRI scans to detect diseases
- Augmented Reality: Used in apps like Snapchat filters, Pokemon Go
- Quality Control: Used in manufacturing to detect defects in products
How Computer Vision Works?
Computer Vision works by breaking down images into pixels and then analyzing patterns in those pixels. The most common technique used in Computer Vision today is Convolutional Neural Networks (CNNs).
CNNs are specially designed to process pixel data. They can detect edges, shapes, textures, and eventually complex objects in images.
Natural Language Processing (NLP)
Natural Language Processing is that field of AI where we try to make computers understand, interpret, and generate human language.
NLP basically means that we're trying to give language capabilities to computers. We're trying to make computers understand and communicate in human languages like English, Hindi, Spanish, etc.
Applications of NLP:
- Machine Translation: Google Translate, which translates between different languages
- Chatbots: Customer service chatbots that can understand and respond to user queries
- Sentiment Analysis: Analyzing social media posts to understand public opinion
- Text Summarization: Automatically generating summaries of long documents
- Speech Recognition: Converting spoken language into text (Siri, Alexa)
- Text-to-Speech: Converting text into spoken language
How NLP Works?
NLP works by breaking down text into smaller units (words, phrases) and then analyzing the relationships between them. The most common techniques used in NLP today are based on Transformers architecture.
Modern NLP models like BERT, GPT, and T5 are all based on Transformers and have revolutionized the field of NLP.
Generative AI in Detail
Generative AI is a type of AI that can create new content. Unlike traditional AI which mainly classifies or predicts, Generative AI can generate completely new text, images, audio, video, etc.
Types of Generative AI:
1. Text Generation
Models like ChatGPT, Gemini, Claude can generate human-like text. They can write essays, answer questions, write code, create stories, etc.
2. Image Generation
Models like DALL-E, Midjourney, Stable Diffusion can generate images from text descriptions. For example, if we give the prompt "a cat wearing a hat and reading a book", these models can generate such an image.
3. Audio Generation
Models can generate speech, music, or other audio content. For example, text-to-speech systems that sound very natural.
4. Video Generation
Models can generate videos from text or image prompts. This is a more recent development but advancing rapidly.
How Generative AI Works?
Generative AI models are trained on massive amounts of data. For example, text models are trained on billions of web pages, books, articles, etc. Image models are trained on millions of images with their descriptions.
These models learn the patterns and structures in the training data, and then use this knowledge to generate new content that follows similar patterns.
The most common architecture for Generative AI today is the Transformer, specifically in the form of Generative Pre-trained Transformers (GPT).
Large Language Models (LLMs)
Large Language Models are a type of Generative AI model that are specifically designed for understanding and generating human language.
What Makes LLMs "Large"?
LLMs are called "large" because:
- They are trained on massive amounts of text data (terabytes or petabytes)
- They have billions or even trillions of parameters
- They require enormous computational resources to train
Popular LLMs:
- GPT series by OpenAI (GPT-3, GPT-4, ChatGPT)
- PaLM and Gemini by Google
- Claude by Anthropic
- LLaMA by Meta
How LLMs Work?
LLMs work by predicting the next word in a sequence. Given some input text, they predict what word is most likely to come next. They do this repeatedly to generate longer pieces of text.
Despite this simple mechanism, LLMs can perform complex tasks like translation, summarization, question-answering, and even coding, because they have learned patterns from their massive training data.
Conclusion
In this comprehensive crash course, we've covered the fundamental concepts of Artificial Intelligence and Machine Learning. Let's summarize what we've learned:
- AI is the broad field of creating intelligent machines
- Machine Learning is a subset of AI where machines learn from data
- Deep Learning is a subset of ML using Neural Networks
- Generative AI is an application of Deep Learning that creates new content
- We explored the three main types of ML: Supervised, Unsupervised, and Reinforcement Learning
- We discussed important applications like Computer Vision and NLP
- We learned about modern technologies like Transformers and LLMs
This foundation should give you a solid understanding of the AI landscape. The field is rapidly evolving, but these fundamental concepts will remain relevant as new technologies emerge.
Remember that AI is a tool that can augment human capabilities, and understanding these concepts will help you make better use of AI technologies in your work and daily life.