We found the 15 best AI projects. Our community contributed many exceptional resources on the subject, and that’s no surprise. Artificial intelligence grew into a newsworthy force this year. Those looking to learn the field should start with AI projects. These tasks introduce fundamental principles in practical ways.
Here, we discuss the best AI projects. This includes where to find the source code and the unique benefits of each. We also offer more in-depth guidance. For a step-by-step guide on a useful AI project, take our course on how to build a Python chatbot. It includes source code to ensure a fast start.
Looking to get started with your own exploration into AI projects? We included more than a dozen options. Read on!
What is AI?
AI stands for Artificial Intelligence and gives machines the ability to assess and execute tasks on their own, without the help of a human. AI makes devices self-sufficient and automated through machine learning methodologies. Before we dive in, you may also want to read up on the difference between AI and ML.
There are four types of AI:
1. Reactive Machine
Reactive machines follow the basic principles of AI. From the name, you can figure out that reactive devices are developed to perceive and react to the world in front of them, or to any interactions it receives from people around them.
A reactive machine doesn’t have any built-in memory. Thus, it can’t store any past experiences and can only perform specific, specialized duties. But, reactive machines are some of the most reliable forms of AI because they react in the same way to the same events every time.
Deep Blue, developed by IBM in the 1990s, is a reactive machine that learned how to beat the grandmaster of chess at his own game. If you’ve never worked on an AI project, then a reactive machine is one of the best artificial intelligence projects to start with!
2. Limited Memory AI Machines
Limited memory AI machines have all the same capabilities as reactive machines, except now with limited memory. With this new feature, limited memory reactive machines can store data and make predictions.
These machines can weigh potential decisions by looking at their memory for clues on what needs to be done next. So, limited memory machines offer a much greater scope of deployment than reactive machines.
Limited AI projects entail training a model to analyze and use data constantly. Or, you can create an AI environment that trains and renews the models automatically. Moreover, when you are working with a limited-memory AI machine, you must create the training data and machine learning model so that it can perform predictions. Moreover, the machine must receive human and environmental feedback, which can be stored in its memory.
3. Theory Of Mind
The third level of AI is a theory of mind, but this version hasn’t been quite perfected yet by tech professionals.
The concept of the theory of mind is to give AI the power of psychological reasoning and understanding. With those features, they can understand what other beings think about them and how the machine can influence others emotionally.
Theory of Mind AI machines can think on their own and make decisions based on what they see and feel. If you’re reminded of the Terminator, we don’t blame you!
As with the following type of artificial intelligence, this one currently only exists in fiction.
4. Self Aware AI
Once you give an AI machine a mind, it can become self-aware and develop a near-human consciousness. At least, that’s the expectation. While we haven’t reached the singularity yet, it’s important to understand the concept.
A self-aware AI machine understands what people need without having to be given any prompts or commands. For example, a self-aware AI robot can make a phone call on its own if a person needs medical assistance.
Sound like Sci-Fi? True self-aware AI technology still only exists in the fantasy world. A futurist may argue that we aren’t far from this now. Want to help with the process? You’ll need to start with a simple AI project first.
Why Should You Learn AI?
Why learn AI? The better question is why wouldn’t you learn AI! Here are some of the benefits of learning about AI:
1. Better Career Options
AI requires programming and engineering for its development. You’ll need solid training and discipline to combine these two areas in your knowledge, but the vast career options and competitive salary possibilities are worthy rewards.
For example, the Bureau of Labor Statistics notes that statistician jobs doubled from 2008-2018. That same trend is expected for machine learning engineers, data scientists, or business intelligence developers. If you’re interested in learning the fundamentals of AI, we highly recommend courses such as Udacity’s Intro to Artificial Intelligence.
2. AI is Versatile
AI isn’t limited to the computer or space industry. You can find AI in your smart TVs, mobile phones, and even speakers. AI is involved in almost every industry, offering you extreme versatility for project ideas and career options.
3. AI is The Skill Of The Century
AI is something that has the potential to replace humans in some roles. But, with those replacements will also come a need for AI professionals in the field. This makes AI the skill of the century, and we humans are still finding new ways to use it.
4. Ability to Take In Huge Amounts of Data
Humans create 2.5 quintillion bytes of data each day, but there’s no way to manage all this data manually. Think about all the data out there that governments, corporations, and businesses need to manage! With AI machines, we can manage large amounts of data and use analysis to make decisions.
5. Better Disaster Management
When a natural disaster devastates a region or a state, citizens take to social media to ask for help. They record videos, photos and, if possible, go live to share information about what’s happening in those affected areas.
These social platforms are AI-enabled search programs. AI helps spread out the news to more people and helps videos go viral to reach a greater audience, contributing to quicker response time from authorities and NGO groups providing aid.
Let’s dive into some of AI’s benefits in our professional lives!
Benefits of AI in the Workplace
Here are a few ways that AI can be a massive advantage to your business:
1. Increased Productivity
Businesses are using AI to improve employee productivity. AI can handle repetitive tasks through automation, giving employees more time to focus on vital work tasks instead of menial ones.
For example, Chatbots are a great AI tool that helps businesses gather information from customers to help employees best address their needs quickly. Chatbots have improved the overall customer experience for many businesses and reduced the number of opportunities for human error.
2. Analyze Big Data into Accessible Insights
Modern-day competitive businesses can’t run and grow without data analysis. But data analysis takes up precious time, necessitating the role of full-time data analysts across major companies. Even then, data scientists and analysts are a burden to a business’s labor costs and often need more tech support and assistance to complete their analysis. Whether you use AI to supplement your data labor or use it as a replacement, you can process and analyze data much faster than without AI.
3. Better Information Security and Vulnerability Management
Now and then, we hear about company and government security breaches that cause millions of users’ personal data to be flushed into public repositories. The reality is, companies are constantly bombarded by information attacks.
AI helps companies identify the perpetrators performing data attacks, and it makes it harder for hackers to steal information. Moreover, AI helps businesses improve their security by learning from the attacks, analyzing security measures, and identifying any system vulnerabilities.
How Are AI and Machine Learning Related?
Many confuse AI and machine learning (ML) as interchangeable, but that’s not the case.
AI can be approached in multiple ways. For example, you can write a computer program that can implement a specific set of rules decided by domain experts. But then again, handcrafting the rules is a tiresome job, and most of the time, it takes a lot of time while still not being fully functional. That’s where ML comes to play: ML makes AI machines more self-sufficient.
Let’s say we’re looking for a way to develop a program that can recognize the handwritten digits within images.
One way to do this is to look at all the images and form a nested if-this-then rule to see which text should be displayed with the particular image. Another approach would use a machine-learning algorithm to use a predictive model based on a thousand labeled images from the database samples.
One final method is deep learning, a subfield of machine learning, used to refer to a particular subset of models that perform specific tasks like image recognition and natural language processing.
So from this example, we see that machine learning makes AI more efficient. But, AI doesn’t necessarily need to be developed with machine learning — it’s more of a nice-to-have than a must-have.
Differences Between Machine Learning And AI
Machine learning helps AI become more efficient, but it isn’t vital to using AI. While both terms originate in the computer science field, there are several differences between the two:
|Artificial Intelligence (AI)
|Machine Learning (ML)
|AI stands for artificial intelligence, in which intelligence in a machine is defined by the acquisition of knowledge.
|ML stands for machine learning which is basically the acquisition of knowledge or a specific skill by the system in which machine learning has been deployed.
|With the implementation of AI, we are looking for an increase in the success rate, not in the accuracy of the work which is being done by the machine.
|The aim of ML is to increase the accuracy, but it does not care whether the success rate has been achieved or not.
|The AI is basically a computer program that does the work smartly.
|The working of machine learning depends on the simple receiving of data and then learning from that particular data.
|In AI, the main goal is to stimulate natural intelligence, which can then be used to solve complex problems.
|The main goal with the deployment of ML is to learn from the data to perform a specific task which leads to the maximum performance of the machine which is doing the work.
|AI is also a decision-maker in complex tasks and then works accordingly.
|When you feed the data on the ML machine, it will read it and learn from it.
|AI will work around to find the best possible solution for the given task and then get it done.
|In the method of creating a process like machine learning, you first need to write down self-learning algorithms.
|AI, in the end, will lead to wisdom and intelligence in machines.
|What Ml learns will go with that solution, no matter if it is the optimal solution or not.
|It is used to mimic what the human would respond when it is put in that situation
|When you work on machine learning, it will lead to knowledge.
Top 15 AI Projects for 2023
Now that you know everything you need to know about AI, let’s dive into some ideas for AI projects for beginners:
1. Housing Price Prediction
Many factors affect housing prices — square footage, neighborhood, inflation, and the number of rooms are a few examples. With AI, you can analyze various datasets at once and input multiple variables to help assess housing prices. We found several options for housing price prediction source code on GitHub.
As with many of these projects, you will need a strong familiarity with Python to get started. For example, we found this source code for using Machine Learning Regression to predict house sales prices.
2. Stock Price Prediction
One of the easiest project ideas for people who are new to AI is a stock price predictor. The stock market has always been a field of interest for AI professionals. Why? Because of how much jam-packed information the stock market has. You can acquire several datasets to work on.
In addition, this project is an excellent opportunity for students looking to work in finance, who want to learn how the finance industry works. Also, keep in mind that the stock market’s feedback loop is relatively short. This results in verifying your forecast algorithm, which you used for the AI.
You can start by forecasting the 3-month price fluctuation of stocks using public repository data and the stocks’ demonstrated inflation through history. These two will be the primary source for the building of your algorithm. You can also use the LSTM model, OTOH, Plotly dash Python framework, which helps you build your stock predictor.
Ready to get started? Check out this stock price predictor source code.
3. Digit Recognition
A handwritten digit recognition system is one way to embark on an AI project that recognizes patterns. In this project, you will be using the Convolution Neural Network (CNN), an artificial neural network used for image recognition and its processing.
You can then take this project one step further by adding in the ability to recognize the alphabet in English, which can then be used for word formation. As promised, we found source code for this AI project. It focuses on using deep learning to identify handwritten characters.
4. Resume Parser
HR managers spend hours skimming through the pile of resumes to find the perfect candidate for a position. But with AI, it’s easy to find the best resume. Create an AI-based resume parser using keyword recognition to assess candidates’ resumes. You can include keywords to catch certain experiences and certifications in the AI system.
Keep in mind though, this screening process can have drawbacks as well. Since many candidates know about keyword matching algorithms, they try to add as many keywords as possible in their resumes, beating the system.
It’s best practice to create an AI system that checks not only the keywords but also the number of times they have been used in a resume to look for anything suspicious. For this, you can use the dataset developed by Kaggle to create the model for this project. The dataset has only two columns, the title, and the information about the candidate present in the resume.
To pre-process the data, you can use the NLTK Python library. After that, you can build the clustering algorithm, which allows you to group the closely related words and the skills which a candidate needs to have to get the job.
Here’s the source code for Open Resume. The immensely-popular AI project contains everything you’ll need for the resume parser.
5. Fake News Detector
Fake news creates chaos in a short time if not handled properly. Many social media platforms such as Facebook, Twitter, and others are working on making their AI perfectly capable of detecting fake news. To create a fake news detector, you first need access to the Kaggle dataset.
After, use the basic data scientist starter pack such as sklearn, pandas, NumPy, and others along with the libraries like transformers and pycaret for this AI project. Your developed system will begin to find patterns that demonstrate if a news post or source is fake. We found this fake news detector AI project source code on GitHub.
6. Instagram Spam Detection
Many Instagram users report countless spam messages from people trying to sell them something, be it an MLL or product.
An Instagram spam detector can help you catch these spam comments and messages; however, there is no proper dataset available on the internet to help your application learn about spam comments.
Start by accessing the Instagram API using Python to get unlabeled comments present on Instagram. Use Kaggle’s YouTube spam collection dataset to train the AI and then use keywords to classify which comments should be marked as spam.
You can also use the N-Gram technique, which assigns the weightage to more frequently used words in spam comments. Then, you can compare these words with escaped comments that are available on the web. Or, you can go with the distance-based algorithm such as cosine similarity. These can have more accurate results based on the type of pre-processing that you apply in the first place.
To get started with this AI project, check out this Journal of Physics paper on Instagram Spam detection.
7. Wine Quality Prediction
Wine quality depends on many features, like the grape type, location, and age. AI machine learning can create a wine quality prediction based on certain inputs, like the year, location, flavor, pH level, acidity levels, and more.
The best part? This innovative AI project takes no more than 2 months of consistent development. Instead of a direct link to existing source code, the challenge with the Wine Quality Prediction project comes from modifying existing code (or starting from scratch). Learners may choose to modify the stock price prediction code or build this predictor from scratch.
Chatbots give customers immediate service when they visit a business’s website. You can create an AI chatbot using well-established frameworks already being used by various MNCs for their websites.
To make an effective chatbot, sketch out the different flows of conversation and find the most common questions asked by the users. Add in the logic before you integrate the modules in the chatbot conversation. Now once the chatbot is made, test it thoroughly before launching it to the public domain if you want to do so. Ask people to test it out to look for potential issues. Train your AI chatbot’s functionality, and decide on the best platform to showcase it!
We include the source code for this AI project in our course, The 24-Hour Chatbot.
9. Email Spam Alert
How does your email account separate useless emails and add them to your junk folder? With the help of AI! You can create an email Spam alert too, by using AI to catch common keywords that are found in spam emails.
This artificial intelligence project uses support vector machines. You can find the source code for an email spam alert here. The project includes 10,000 email in a dataset, which you can evaluate as a CSV file.
10. E-commerce Product Recommendation System
You might notice ads on your social media pages for e-commerce products. Why? Because of AI.
The AI algorithm relies on your previous purchases and page visits to create e-commerce recommendations. On the other hand, many businesses are now coming up with in-moment suggestions. As they use AI to analyze how each user on their website is interacting and choosing the products.
To create an E-commerce product recommendation AI project, you’ll first need an already-built framework that uses machine learning algorithms.
There are two categories of machine learning algorithms for recommendation systems. First is collaborative, and second is content-based filtering. But, if you are looking to make an innovative version, you should definitely go with the combination of both.
There are many ways to use artificial intelligence for product recommendations. Here’s the source code for one such AI project.
11. Password Checker
It’s hard to find a good password that can protect your data from hackers. Nowadays, websites ask users to write a password that has a unique character, a numeric digit, and both a small and caps alphabet. This AI project is discussed in detail in our course, Python with Dr. Johns.
Some websites have password checkers to measure the security of your password, a cool AI project idea for you to try! Your application can test password security while also finding patterns that make passwords weak.
12. Autocorrect Tool
You likely have an autocorrect tool on your phone that corrects typos as you text. Autocorrect tools also exist within Google Docs and Microsoft Word. And, Grammarly is a downloadable autocorrect tool that you can use to edit any piece of writing.
Here’s how you can create an autocorrect tool as an AI project. First, you need to use Python, where the TextBlob library will come in handy. The library comes with a function called “correct().”
If you use this function on a word or line, it will tell you if the text is correct or not, and replace the incorrect word with the correct one.
Keep in mind that the TextBlob isn’t a perfect library, and sometimes it does have issues finding the misspelled word. In some cases, the algorithm was not able to find a misspelled word before the initial word was correct.
13. Hand Gesture Recognition Model
This project has been quite a favorite for newbies, and it is easy to develop using web applications in Python. Start with the hand gesture database on Kaggle. This database comes with more than 20,000 labeled gestures!
Train your application with the use of VGC-16, and you can also add in the OpenCV to collect the live stream of video data. This video data will then be used to detect and make predictions on hand gestures in real time.
14. Price Comparison Application
Have you ever seen a dress in the store and wanted to know where you can find it at the lowest price?
Well, this AI project will help you find the place with the best deal on your dress. But, the complexity of the project depends on how much effort you want to put in. You can make this application to scan the product and then find it on the internet with the “lowest price” filter. Or you can simply type in the product, and the application will search for the product’s price on online websites in ascending order.
Use an algorithm or a library that can identify the specific objects present in the image. For example, if someone is uploading a picture of a formal dress, the algorithm should be able to identify the color and style of the dress. You can also use VGG-16, the pre-existing database of item descriptions. Once you build a model, you can provide the user with the choice to add in extra details about the product (brand, season, size, etc.)
15. Create A Game
AI bots within a game learn from the user’s moves. For example, if you play chess against a computer, the AI bot can become better and better, making it harder to beat. Try creating a simple game as your final AI project.
Python AI Projects and Raspberry AI Projects
Python for AI
Python is a popular programming language used widely for web development, AI, machine learning, operating systems, video games, and mobile application development. It has a wide range of pre-built libraries that makes the development of AI projects easier and simpler.
Python’s Numpy library is used for scientific computation, Scipy for advanced computing, and Pybrain for machine learning.â€‹â€‹ Therefore, Python is considered one of the best languages for artificial intelligence. Also, it is one of the most flexible and popular languages for use in various technologies and platforms with the least tweaks in code.
Some popular Python libraries to kickstart AI projects are Matplotlib, Pandas, Numpy, scikit-learn, and iPython Notebook. The Numpy library is used as a container that stores generic data, like N-dimensional array objects, Fourier transform, tools for integrating C/C++ code, random number capabilities, and many other functions.
Pandas is an open-source Python library that provides easy-to-use data structures and analytical tools to use in your AI projects. Matplotlib is a 2D plotting library used for generating publication-quality figures. You can use this Python library with up to 6 graphical user interface toolkits, Python scripts, and web application servers.
Below is the list of the top 5 AI projects that utilize Python:
1. Fake Review Detector
Fake Review Detector is a beginner-level Python AI project. You can build a fake review detector by creating a classifier that can recognize fake reviews. This fake review detector ensures that a site has no fake reviews.
2. Traffic Analyzer
A traffic analyzer is yet another best Python AI project idea. In this project, you need to build a traffic analyzer that can suggest the best and optimal path for reaching a specific destination. In addition, it considers various factors while suggesting the optimal path, such as traffic density, the mode of travel, and the length of a path. One of the best examples of a traffic analyzer is Google Maps.
3. Handwriting Recognizer
To build a handwriting recognizer, it is essential for you to have knowledge about computer vision. Computer Vision is a central aspect of artificial intelligence. A handwriting recognizer system can recognize the written content.
4. Spoiler Blocker
In the spoiler blocker project, you have to build a tool that detects spam and blocks them automatically. You should have in-depth knowledge of natural language processing (NLP) to build a spoiler blocker.
5. Fire Detection and Localization through Camera
Another best Python AI project is Fire Detection and Localization through Camera for intermediate-level developers. You need to build a tool that can detect fire and locate it through cameras’ feeds. Developing this project requires a strong understanding of a Convolutional Neural Network (CNN).
Raspberry Pi for AI
When it comes to artificial intelligence and machine learning, Raspberry Pi is considered one of the most robust tools. Raspberry Pi is a credit-card-sized computer or a series of single-board computers and can be plugged into a computer monitor or TV. It is a great choice for embedded projects and smart robotics because of its processing power, low power requirements, and match with a small form factor.
Raspberry Pi has its application in a wide range of fields, such as home automation, IoT, machine learning, artificial intelligence, and a few to name. Below are some popular Raspberry Pi AI project ideas for beginners and seasoned professionals.
1. Twitter Bots
Twitter Bot is one of the widely used social media platforms. For many people, handling their Twitter accounts can be a cumbersome task. Raspberry Pi bot has made the cumbersome task of handling a Twitter account easier that can send automated tweets on Twitter.
2. Smart TV
Smart TV is yet another best Raspberry AI project idea. It is possible to build a fully-functional smart TV using Raspberry Pi and a monitor. Kodi is best to use for developing this project. It is an open-source media player software application software.
3. AI Assistant
We can use Raspberry Pi to build an AI assistant. In addition, you can use Google Assistant and Google Cloud SDK. Firstly, you need to sign up on Google Assistant and set the audio for your account. But you need to ensure that the Pi board you use should be authorized for this project.
4. Print Server
Using Raspberry Pi, you can convert your simple printer into a wireless printer. You can use that wireless printer with any device you connect to it. To build this Raspberry AI project, you need a Common Unix Printing System (CUPS). Also, you need the latest version of Raspberry Pi, i.e., Raspberry Pi 3.
5. Weather Station
If you are a beginner, building a weather station using Raspberry Pi is the best AI project idea. It is possible to turn your Raspberry Pi board into a complete weather station. For this, you will a BME280 sensor that can analyze temperature, pressure, and other parameters of weather. In addition, you will get Oracle Raspberry Pi Weather Station for its APIs.
We evaluated the best AI projects. In our research, we considered complexity, usefulness, and whether we expect to see more like this in the future. We also sought to highlight different aspects of AI projects. We included those with machine learning, natural language processing, and other fundamentals. Choose one of these AI projects to get comfortable with this new field and expand your knowledge.
Do you have any other ideas for cool AI projects? Let us know in the comments section! Till then, keep on programming and keep on innovating.