Artificial intelligence is one of the most sought-after fields of research in 2022. All sectors are rushing to incorporate AI and machine learning to make their processes more efficient, resilient, and productive.
Hence, it is only natural that more and more students and professionals are looking to benefit from the ever-expanding realm of artificial intelligence. Some of the best applications of artificial intelligence so far include:
- Image recognition technologies
- Product recommendation systems
- Search engines
- Spam filters
There are several means of learning artificial intelligence, such as books, online tutorials, offline courses, and projects. Probably the best mode of learning is AI books. They are your companion for all seasons.
However, working on artificial intelligence projects will allow you to better understand AI and check how much you’ve learned so far. Therefore, here, we are going to discuss some of the best AI project ideas to get you started or going if you are already on the path of becoming an AI developer.
Artificial Intelligence Projects with Source Code
AI projects come in all shapes and sizes. Working on them is much more complex than working on programming projects because you have so many options to choose from. However, that’s not always the case.
Anyways, to make this list of the best artificial intelligence projects legible, we will divide it into beginner, intermediate, and advanced levels so that you can choose where to start. That said, let’s start with artificial intelligence projects for beginners.
Artificial Intelligence Projects for Beginner
For newcomers and starters, beginner AI projects are the perfect launchpad. These projects don’t necessitate going in-depth into artificial intelligence or machine learning. Instead, they allow learners to understand the mechanics of AI. Below we have compiled a list of four beginner-friendly artificial intelligence project ideas:
1. A Resume Screening Tool
A beginner-friendly AI project could involve designing a tool that can screen resumes based on job requirements. This is an excellent tool that has been used by full-fledged organizations to cut both effort and time in selecting the best candidates for a specific job profile.
That’s what we can call a resume screening tool. You can also call it a resume parser or resume screener for the sake of simplicity. Anyways, it will work on the basis of identifying specific keywords in candidate resumes.
For accomplishing this project, you need to have a sound understanding of clustering algorithms and natural language processing (NLP). The basic task here is to filter resumes based on keywords. Keep resumes with the required keywords and toss away resumes that don’t have them.
To work on the resume parser project, you need a resume dataset. There is a multitude of them available at Kaggle. Otherwise, you can use datasets from other platforms too. Depending on the dataset, you will get many columns, including job title and bio.
As the data is in the form of text in the dataset, you need to preprocess it. For that, you can use the NLTK library or some other ML library with NLP capabilities. Next, you need to develop a clustering algorithm that filters keywords, skills, and closely-related words to keywords.
2. Instagram Spam Detector
Another great artificial intelligence project idea for a beginner to try their hands on is an Instagram spam detector. You can leverage the capabilities of machine learning to develop a spam detection model that can distinguish between genuine and spam comments.
It’s a useful AI project that you can keep to yourself or develop and sell as a product (yes, that might be too ambitious, but it can work out). For the required dataset, you can either download one from the web or scrape the web and build one on your own. Do whatever you find convenient.
Now, there are multiple approaches to accomplish this beginner-level AI project. One approach is to add keywords for classifying words that are akin to spam comments.
Use N-Gram to provide weightage to words commonly appearing in spam comments. Next, compare them with scrapped comments from the web. Alternatively, you can use cosine similarity or some other distance-based algorithm.
Please note that in order to enhance the performance of the algorithm employed, you need to eliminate punctuation, stop words, and whitespaces from the dataset. For superior results, you can use a pre-trained model like BERT or ALBERT. What makes these models powerful is that they consider factors such as coherence and the context of the sentence.
3. Animal Species Detection
Computer vision is a distinct field of machine learning, which, in turn, is a subset of AI. There are several beginner-level AI projects that you can develop using it, and one that we suggest is an animal species detection system.
Here, you need to simply use images to train an ML model to understand the difference between a variety of animals, such as dogs, cats, horses, hens, sheep, cows, and so forth. Technically, identifying animal species is a multi-class classification problem.
Thankfully, being a beginner-friendly project, you can find several datasets on the web, such as Kaggle and GitHub. One dataset that you can try is Animals-10, which is available on Kaggle. It has data on ten different animal species, namely butterfly, cat, chicken, cow, dog, elephant, horse, sheep, spider, and squirrel.
If you want to focus on a Pythonic build, you can use a popular pre-trained model, VGG-16, and load it into Python using the Keras library. It is a CNN architecture trained on ImageNet that has more than 14 million images.
Once you’ve successfully loaded the VGG-16 model, you can train it using the ML dataset that you chose earlier. For instance, if it was the Kaggle Animals-10 dataset, then you need to train VGG-16 for identifying among ten different types of animals.
4. Autocorrection Tool
MS Word and Google Docs come with the autocorrect feature that generates suggestions for correcting grammatical mistakes and writing errors.
Grammarly is a popular tool among content developers that is capable of detecting grammatical, syntactical, and – to some extent – logical errors along with spelling mistakes. It is not an autocorrect tool, but it has the functionalities of one.
A basic autocorrect tool can be developed using artificial intelligence. You can develop it using many technologies, but Python is a good option as it already has a library for developing it, TextBlob.
TextBlob comes with a correct() function that will check the word (a string of text) for spelling mistakes and then autocorrect it to make a correct word closest to the original word.
There are certain limitations to the Pythonic library, though that you can overcome with building your own model using BERT or some other pre-trained NLP model.
More Beginner-Level AI Projects
Other than the projects mentioned above, here are some more beginner-level AI project ideas for you to try out:
- Fake news detection system
- Fake product review identification system
- Language translator
- Object detection system
Intermediate Artificial Intelligence Projects
Once you’re done with many generic beginner-level AI projects, you need to proceed with intermediate-level artificial intelligence projects. These are neither too difficult to develop but neither too easy. Thus, you need to invest a good amount of research and effort for them.
Moreover, you need to have a robust knowledge of many advanced AI and machine learning concepts, like ANNs, CNNs, transfer learning, visual recognition, and so on. Following are our three picks for best intermediate AI projects:
5. Ethnicity Detection Model
An interesting artificial intelligence project idea for intermediate (and also for advanced-level) developers is an ethnicity detection model, which, as its name suggests, will identify the ethnicity of a person from a given picture.
You need to have a good knowledge of computer vision, artificial neural networks (ANNs), and convolutional neural networks (CNNs) to accomplish this AI project. However, if you don’t want to use ANNs and CNNs, then you can use transfer learning as an alternative.
Accuracy is an important factor for developing the ethnicity detector. That’s because there are so many ethnicities around the world, and many of them share several similarities. Hence, to keep the complexity of the project low, you can restrict ethnicities to 3 or 4. Nonetheless, you’re free to go with a higher number if you want.
Use the UTKFace dataset at Kaggle or some other dataset available online for training the model. Models developed using UTKFace allow developers to achieve an impressive accuracy of 0.80. Also, use the Ethnicity Detection in Python GitHub resource.
6. Stock Price Predictor
If you’re already an investor or would like to invest in securities and equities, like stocks, then you can kill two birds with one stone with a stock price predictor. Working on this project will allow you not only to put many AI and ML techniques to test but also create something helpful to understand the price movement in stock markets better.
Today, there are many apps and tools that leverage the capabilities of machine learning and AI to predict the future price fluctuations of a stock.
Share market is a goldmine of data for ML experts. Moreover, you can try different types of datasets to work on the stock price prediction model. Because the feedback cycles of the stock market are transient, it can help you to validate your predictions.
You can try to predict the price movements of a particular stock in a particular time period, such as for the upcoming month or 3 months or 6 months. For accomplishing this project, you need to collect data from the stock’s reports.
7. Product Recommendation System
A product recommendation system is a typical example of AI. It is also known as the customer recommendation system. It’s a popular component of eCommerce websites. The idea is simple, provide the customer with relevant product suggestions based on their recent purchases and product search history.
Developing a product recommendation system is a good idea to learn the various concepts involved in AI and machine learning. To know more about how such a system works, look no further to Amazon. In addition to making life easier for customers, a product recommendation system also helps organizations to deliver a better customer experience and increase their sales.
The data that is fed to the model will be the browsing history and recent purchases of the customer. You can either prepare data on your own or download one of the already available on the web.
More Intermediate-Level AI Projects
That sums up our list of intermediate-level artificial intelligence projects. Some more intermediate-level AI project ideas that you can tinker with are:
- Age detection system
- A chess game
- Next word predictor
- Price comparison tool
- Traffic predictor
Advanced Artificial Intelligence Projects
This is when things get really interesting and complex! Advanced AI projects will not only allow you to test your learning but also have the potential to create real-world-ready systems that can prove to be more than just projects for demonstration.
In this section, we will discuss only two projects. We will start with looking into developing a sign language recognition app in Python and then proceeding to build a system that can automatically mark attendance. So, let’s start.
8. Sign Language Recognition (Python)
Sign language is complex, but many people learn it to better communicate with people having hearing and speaking disabilities. However, not everyone knows it. An advanced AI project idea is a sign language recognition system. The one we are going to discuss here leverages Python.
Working on this project is challenging. Hence, you need a good understanding of many machine learning concepts, such as model training, perceptron, and multi-layer model development. Now there are various ways to accomplish this model.
One way is to leverage the World-Level American Sign Language dataset. It has more than 2,000 classes of sign languages. For training your model, you need to extract frames from the data and then load the Inception 3D model trained on the ImageNet dataset, which is a visual database of more than 14 million hand-annotated images.
You need to train a few dense layers on top of the model using frames from the loaded dataset. By doing so, you can generate specific text labels for specific sign language gesture image frames. Once you’re done building the model, you can deploy it to help normal people communicate effectively with people who have hearing or speaking impairments.
9. Automatic Attendance System
Although it might sound like an easy task to develop an automatic attendance system, it is actually quite complex and challenging to do so. It involves dataset loading, model training, face recognition, and more.
The system must be able to recognize the faces of the students or employees and then mark their attendance accordingly. Therefore, you need to have a camera that can take pictures of the candidates and then compare them with the images stored in a database and mark attendance accurately.
More Advanced-Level AI Projects
Other than the advanced-level artificial intelligence projects explained above, here are some more advanced-level AI projects to try:
- Colour detection
- Detecting violence in videos
- Fake product review monitoring system
- Hand gesture recognition model
- Sports betting apps
- Text generation model
Artificial intelligence is a lucrative career path in 2022 and beyond. However, it requires dedication, creative thinking, knowledge, and lots of practice. Since the tools and techniques for AI are evolving quickly, an AI developer should stay abreast of the latest developments.
The abovementioned artificial intelligence projects will help you learn AI better by putting your knowledge to the test. However, please note that the projects that we have mentioned above are mere ideas. Hence, don’t shy away from being creative while working on them. Wish you all the luck!
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