Home Tech 9 Best AI Projects Idea For Beginners (With Source Code)

9 Best AI Projects Idea For Beginners (With Source Code)

Even though AI has been around for over six decades as an academic and scientific theoretical stuff, it has gained the most traction in the last few years.

From self-driving cars to virtual doctors, Artificial Intelligence is everywhere. Not only that but the smartphone applications that you use, such as Spotify, Google Search, or more, are using AI in some sense.

According to a PwC report, the AI industry could add up to $15.7 trillion by 2030 to the global economy. Since the industry is growing, the demand for AI engineers is also increasing, and it is also a well-paying industry. According to Glassdoor, the average pay scale for an entry-level AI engineer is between $82,000 to $170,000.

This means this is the perfect time to start if you want to build a career in AI. However, AI is a very vast field, and there are plenty of areas where you can choose to specialize.

For example, if you are into facial recognition systems or image generation, Computer Vision is the field you can choose to work on. On the other hand, if you want to design an AI model that can communicate with humans, Natural Language Processing (NLP) can be the right field.

If you are already an engineer or have professional qualifications, getting a job in the AI industry will be a bit easier for you. However, that doesn’t mean that you cannot get into this industry if you don’t have a technical degree.

All you require to have is a portfolio where you can showcase some interesting AI projects. If you can showcase your contribution to some open-source projects, that will add the cherry on the top. Building these AI projects will not only help you land a good job but will also help you sharpen your skills.

To help you guys with some AI project ideas, we have prepared a list that we are going to share with you. This list contains the idea, how you can execute it, and more.

So, without any further ado, let’s get started on the list of the best AI projects.

Best AI Projects For Beginners – Our Top Pick👌👌

1. Handwritten Digit Recognition System

What is it, and why is it needed?

Even though most industries have adopted digitalization, there are still a few areas where paperwork cannot be replaced. For example, in regions where people are not that educated about the internet or technology, filling out online forms can be a daunting task for them.

If we can create an AI model that can recognize handwritten digits and process them to computers, we can save quite a lot of time manually feeding the data.

Best AI projects

Now, the biggest problem here is that digits and characters written by humans cannot be the same. Each individual has different handwriting, and therefore, the size, shapes, curves, styles, and more vary from individual to individual.


To create an AI project like this, you need to have knowledge of artificial neural networks.

For this AI project, you have to train a convolutional neural network (CNN) on the MNIST dataset. This dataset contains a total of 70,000 images of handwritten digits ranging from 0 to 9.

Handwritten Digit Recognition on MNIST dataset | Machine Learning Projects 5 | ML Training | Edureka

Source Code:-

You can surely give it a try to design this system on your own, but if you want some idea of how it will be designed, you can use this source code.


This handwritten digit recognition system is one of the most remarkable AI projects that can be used in a variety of areas.

For example, it can be used for bank cheque authentication, taking quick notes, reading filled forms, etc. You can even use it for designing calculators, handwriting gestures and input on digital devices, and more.

2. Product Recommendation System

What is it, and why is it needed?

The world today demands personalized experience, whether it is about smartphone applications, e-commerce stores, news sites, social media, advertisements, or anything else. We don’t want to indulge ourselves in things we are not interested in, and whenever an app or website fails to do so, it starts losing its users.

Now, if we can create a system that will remember what the user is interested in based on his past activities and offer them relevant, individualized, and accurate recommendations, we can manage to enhance the user experience.

When we start offering such recommendations, it can help us produce a large amount of revenue or at least can help us stand out amongst the competitors.


This system entirely relies on the previous purchases, searches, interactions, and the items in the cart of the user. Based on that, you can create a system that will analyze all these aspects and then provide them with in-moment suggestions.

The first thing you will need to create a recommendation system is the data. The more data you can collect, the better recommendation your system can provide.

9 Best AI Projects Idea For Beginners (With Source Code)

Once all the essential data have been collected, you have to filter it to extract the most relevant information that will help you provide personalized recommendations.

To create a system like this, you will need to use an already built framework that uses and applies machine learning algorithms.

There are four types of machine learning algorithms that you can use – Content-based Filtering, Collaborative Filtering, Complementary Filtering, and Hybrid Recommendation Systems.

Content-based Filtering basically analyzes the user’s interest, past browsing history, products bought, and more and then creates a list of recommendations.

On the other hand, the Collaborative Filtering techniques use all this information, compare it with the interest of other users with a similar taste, and then suggest recommendations. 

The Complementary Filtering technique analyzes the probability of two or more items bought together and provides recommendations based on that.

For example, let’s say you are buying a smartphone; there are good chances that you will also buy a screen guard, back cover, or an earphone. Lastly, the Hybrid System basically uses all these techniques to offer personalized suggestions.

Recommendation systems overview (Building recommendation systems with TensorFlow)

Source Code:-

You can surely give it a try to design this system on your own, but if you want some idea of how it will be designed, you can use this source code.


The recommendation system is widely used in e-commerce and streaming applications and services such as Amazon, eBay, YouTube, Hulu, Netflix, Spotify, Tidal, and more because it allows users to find stuff according to their likings and tastes.

It not only helps users find the item they may be interested in but also helps companies to increase their product reach, profitability, and more.

Further, it can also be used for targeted advertisements, news, and other areas.

3. Lane Line Detection

What is it, and why is it needed?

Self-driving vehicles are one of the finest and most innovative innovations in AI. These autonomous cars can do almost anything that an experienced driver can do, even sometimes better than that. However, it is very important to train this system properly. Otherwise, it can lead to a catastrophic situation.

One of the most important aspects of these self-driving vehicles is the lane detection system. It is quite important because if our system is able to identify lanes properly, the vehicle will understand where it can go and avoid the risk of getting off the road or running into other lanes.

Best AI projects 2

If we can create an efficient system for this, this system can prevent the car from drifting off the road or driving lane.


To create this system, you can use Computer Vision technology as it has come a long way, thanks to the advances in Deep Learning.

Computer Vision can be used to recognize different types of objects in images. For this, computer vision examines and compares millions of examples and cleans the visual patterns that can define each object.

You can create this project in Python using the OpenCV library. This is an open-source optimized library, developed by Intel, mainly for real-time Computer Vision usage, such as detecting lane lines. This library features the Python, Java, and C++ interfaces and supports Windows, macOS, Android, iOS, and Linux platforms.

This OpenCV library can easily detect markings on the lane, meaning you can use it for identifying road lanes. First, you have to find all the white markings on a lane, and then with the help of frame masking and NumPy arrays, you have to mask the rest of the objects.

After that, you can use the Hough line transformation technique to detect the road lane lines. You can even use other Computer Vision methods such as color thresholding or any other to identify the lane lines.

OpenCV Python Tutorial - Find Lanes for Self-Driving Cars (Computer Vision Basics Tutorial)

Source Code:-

We recommend you design this lane line detection system on your own, but if you want some help, you can use this source code.


This lane line detection system will primarily be used in self-driving cars and vehicles. However, it can further be used in line-detection robots, racing cars, games, and other areas.

You may like to read our guide on 47 Top Machine Learning Blogs To Improve Your Skills.

4. Plagiarism Checker

What is it, and why is it needed?

Plagiarism is one of the biggest problems for content creators, and the saddest part is that it is quite simple and easy to do, especially when it is written content.

There are plenty of websites that have created scripts or tools that will automatically mine the content available on your website and publish it on their own in no time.

It not only leads to reputation damage for brands but also has quite a bad impact on search engine performance. The worst that can happen is that your website will get penalized by search engines for using copy content, even though you produced that on your own.

If we can make a tool that will automatically analyze all the websites available on the internet and identify the plagiarized content, it will be a lifesaver.


There are plenty of ways to create a plagiarism checking tool to identify copied content, both with AI and without AI. Since here we are mainly talking about the AI project, we will explain how it can be achieved using Machine Learning.

To create this project, you can use the Amazon SageMaker service, one of the most popular cloud-based Machine Learning platforms.

Along with that, you can use the Amazon S3 service and Amazon IAM service for file storage and other purposes. You can use any other cloud service such as Azure or anything else.

After that, we have to install some Machine Learning and Data Processing libraries, including NumPy, Pandas, and Scikit-Learn.

Source Code:-

You can try to create this plagiarism checker tool on your own, but if you want some help, you can use this source code.


The most common use of plagiarism checkers is in the blogging and writing industry. People such as content creators, developers, publishers, freelance writers, educators, and others can greatly benefit from it.

This will make it easy to identify if your writer has provided you with copied content or if someone on the internet is using your published work without your consent.

5. AI Chatbots

What is it, and why is it needed?

We live in a fast-paced world where everybody wants to get the information they are after very quickly and easily. When we don’t get it on time, we often get frustrated.

If we put this into a business scenario, your customers may shift to your competitors if you fail to provide a top-level customer service experience.

Not everyone can hire a massive team for customer support to tackle this issue. In such situations, AI chatbots can be a lifesaver.

These chatbots are nothing but applications that enable automatic conversation between AI bots and humans (your customers).

These chatbots can communicate via text or speech like Alexa, Siri, etc. the best thing about these chatbots is that they can be available 24/7 without any worry and will help your customers by solving their queries, helping them navigate through the app, and more.

Even though these chatbots are quite advanced and offer dozens of great benefits, these AI chatbots cannot replace real humans. However, research has shown that the businesses that have implemented AI chatbots have shown significant growth.

Now, you don’t actually need to use AI to build a chatbot. You can write a script, and that will get the job done. However, one of the biggest limitations of these script-based chatbots is that they cannot solve problems and scenarios that are not written in the script.

This is one of the reasons we need AI chatbots, as these chatbots not only learn with more and more training but also react to the meaning of the whole question and interact like a human.


Initially, I recommend you start with a very basic chatbot. You can get ideas from chatbots available on other websites. You can construct it using Python or any other language of your preference.

Once you have constructed a simple chatbot, you can further refine it and create a complex version capable of performing complex tasks. You can use Natural Language Processing for this purpose as it will help computers and algorithms to understand what the user input is and process that data.

9 Best AI Projects Idea For Beginners (With Source Code)

It can understand various languages and even audio signals and convert that data to a machine-understandable language. You will also need some pre-trained tools, packages, and more to create an efficient and intelligent chatbot.

Creating Chatbots Using TensorFlow | Chatbot Tutorial | Edureka | AI Live - 1

Source Code:-

You can create a basic chatbot very easily on your own, but if you want some help to train it to solve advanced problems, you can get some ideas and inspiration from these already built AI chatbots. (Source Code)


Chatbots are very useful for businesses that require customer support, IT helpdesk, marketing, and more. These chatbots can even help you with tasks such as hotel booking, food ordering, and more.

6. Pneumonia Detection

What is it, and why is it needed?

Pneumonia is still a very big threat, and it mostly happens when the infection inflames the air sacs or one or both lungs. These air sacs may get filled with fluid like pus that causes cough with pus or phlegm, chills, fever, and even difficulty in breathing.

The biggest problem of pneumonia is that even though it is not that serious, it can be life-threatening for some people going through other severe medical conditions.

The problem here is that when we take X-Ray images to identify diseases like pneumonia, cancer, or others, the X-ray images offer low visibility, making it difficult to assess the situations sometimes. In addition to that, the position, size, and shape of pneumonia also play a very major role in the treatment.

Due to the poor visibility of X-Ray images, it becomes difficult to detect and understand the severity of the situation. Now, if we can create a tool that will process the X-Ray image and identify pneumonia along with the infectious area with maximum accuracy, doctors can give a proper treatment that could save some lives.


For this, you have to create a Deep Learning model that will be able to analyze the X-Ray image and then can tell us whether the patient has pneumonia or not. Plus, it will also be able to identify how severe that patient’s condition is.

You can use the VGG16, an advanced and broadly used Convolutional Neural Network architecture. This architecture is used for ImageNet, a huge visible database mission used in visual object recognition software research.

You can use any other CNN architecture such as InceptionNet, DenseNet, or others, but VGG16 has shown the most accuracy during the test.

9 Best AI Projects Idea For Beginners (With Source Code)

You also have to use the transfer learning technique that uses the stored and gained knowledge to solve other problems.

This will make your model more and more efficient with every new test. Other than that, you can use the Keras Python module that runs on the TensorFlow library, SciPy, and glob Python module.

COVID And Pneumonia Detection Using Chest X-Rays | GL Projects Showcase | Great Learning

Source Code:-

If you follow the solution mentioned above, you will be able to create a pneumonia detection tool with some hits and trials. However, if you are stuck on something and need some guidance or ideas, you can use this project. (Source Code)


This pneumonia detection model is beneficial for the healthcare and medical domain for detecting pneumonia disease and its severity level. Not only that, but you can modify and train this model to identify other serious diseases such as cancers, tumors, etc.

You may like to read our guide on 47 Top Machine Learning Blogs To Improve Your Skills.

7. Language Translator Application

What is it, and why is it needed?

There are over 7100 languages spoken in the world today, and each one of them has a significant contribution to making the world a beautiful and diverse place. Obviously, English is considered the global language because the majority of developed countries use it, but not everyone understands it.

Now, let’s say you are traveling to a new country and people there don’t understand English. What will you do in that situation? Let’s put it in a different scenario.

Let’s say you are planning to shift to a new country for education or work purposes, but you don’t understand their native language. In such a scenario, a language translator application can be a lifesaver.

The biggest problem here is that it is quite tough to create a model like this because your model needs to have a thorough understanding of the texts in most general scenarios.

For example, let’s consider the French word “doux.” This word has several meanings in English, such as “sweet,” “soft,” and gentle.3

Now, if you want to make sure that your model is efficient and correct, your model needs to understand the different meanings of the word in different scenarios.

The language translator application in AI looks quite simple and easy to create an AI project, but it is quite complicated and, to be frank, is not a basic to intermediate level project.

I will say that this project falls somewhere between intermediate to advanced level AI projects.


Now, there are two ways to design this application. Let’s start with those who are beginners and want to create this project.

You can use Deep Translator, a tool designed by Nidhal Baccouri that helps you integrate all popular language translators such as Google Translate, Microsoft Translate, Yandex Translate, DeepL Translate, and more to design a translator that is capable of translating most of the languages accurately.

After that, you can modify it to deliver better performance.

However, if you are an intermediate to advanced level AI engineer, you can use the Natural Language Processing and transformer models to develop this project.

A transfer model is necessary because it allows us to store a database of translation rules and translates straight when the rule matches.

It extracts the meaning of each word and its significance and translates to a version that makes a proper sense of the sentence. For this, you can use any pre-trained Python-based transformer model. You can also use the GluonNLP library to load and test datasets.

Source Code:-

If you are a beginner or intermediate user, you can follow the below-mentioned guide to create an AI-based language translator.

However, if you want to build your own app from scratch using AI, we don’t have a source code for that. You can follow the tips we shared above and do your own research to find out how you can build it from scratch.

If you can make it independently without using any other API, that will be a billion-dollar project for sure. (Source Code)


A language translator is used to translate different languages. It can be used to translate business documents, communicate while traveling, blogging, learn new languages, and other areas.

How Google Translate Works - The Machine Learning Algorithm Explained!

8. Face Recognition App

What is it, and why is it needed?

We humans have different face shapes and sizes, and we identify each other by looking at our faces, not fingerprint or iris scans. This means that face recognition technology is more natural to integrate into our daily lives.

A facial recognition app is biometric software that can uniquely identify and verify a person by analyzing the structure of their face. This technology is vastly used in security practices, but there are plenty of other areas where you can use it.

How does facial recognition work?

This concept has received a lot of attention because it has the potential to be used in a wide range of areas such as law enforcement and other enterprises’ needs. A few benefits of creating this system include improved security and fast and non-invasive identity verification.


A face recognition app can be created using Machine Learning. You can create a model that could detect a human face and all minor details of his face.

It should start with the eyes, followed by eyebrows, mouth, nose, iris, nostrils, forehead, and more. Once this data is collected, your system should compare it with the database and give the output.

9 Best AI Projects Idea For Beginners (With Source Code)

Currently, there are four common techniques that you can follow to create a face recognition model. These techniques include feature-based recognition, appearance-based recognition, knowledge-based recognition, and template matching. Each of these techniques has its own pros and cons.

The feature-based method focuses on facial features such as eyes and nose to detect the face. On the other hand, the appearance-based technique relies on statistical analysis and machine learning to compare the characteristics of a face and give output.

The knowledge-based technique follows the predefined rules to identify the face. This is quite a challenging method and requires effort, but it is also quite accurate.

The template-based method just compares images stored in the database to identify the face. It is an easy to execute technique but often fails due to variation in face shape, pose, and scale.

Source Code:-

You can give it a try to design this system on your own, but if you want some idea of how it will be designed, you can use this source code.


Face recognition applications can be used in a variety of areas such as smartphones or automobile security, access control, education, immigration, other law enforcement tasks, healthcare, and more. It can also be used for smart advertising, identifying people on social media, casinos, etc.

9. Automation System

What is it, and why is it needed?

An automation system is a system where machines or programs do simple, repeatable tasks that follow the instruction set by users. Automation tasks are boring, repetitive, and predictable tasks, such as making parts for an automotive vehicle.

The biggest drawback of an automation system is that it cannot do anything that isn’t described in the script. However, we can eliminate this with the help of AI.

An AI-based automation system can take broad rules outlined by humans and make its own way to achieve success.

With time, this system can learn what works and what does not, and therefore, its performance improves automatically with time. One of the best benefits of such AI projects is going to be the speed and efficiency with the ability to find better ways to do the same work.


You can use the AI to build software that could automatically do tasks such as scheduling calls, taking attendance, processing transactions, etc. This system is not going to be limited to this only. Instead, there are a lot of possibilities.

For example, it can be used in industries to replace human workers.

To build a system like this, you will need skills like Natural Language Processing, Computer Vision, and more. You can also use Deep Learning algorithms and transfer learning techniques to ensure that your model will learn automatically with time.

Build 4 Python Automation Projects With Machine Learning

Source Code:-

GitHub or any other platform doesn’t have any such model designed yet. However, here are a few projects (Get Here) and threads that can help you do thorough research about this topic.


A system like this can be used in a plethora of areas, especially in businesses. You can use it in banking, finance, healthcare, education, manufacturing, and others.

Closing Comments

There you have it – a list of the 9 best AI projects.

In this list, we have included some beginner-friendly AI projects as well as some intermediate to advanced level projects. All the project ideas mentioned in this list are in demand, and if you include these projects in your portfolio, they will surely help you get a good job.

Even some of the projects, such as Language Translator Application or Automation System, can be used as a completely new startup idea, or you can create such a system and sell it for a billion dollars.

With that said, here I am wrapping up this article. If you have any questions, feel free to shoot them in the comment section below. Our team will try to respond ASAP.