Real-World AI Projects That Will Change The Future
List of top AI projects that can be applied to the problems of the real world for a wide range of Data Scientists, from beginners to experts.
What was once a thing of the imagination and seen in Sci-Fi movies is now a reality. All with the help of AI (Artificial Intelligence). Bots like C3PO translating out-worldly languages, self-driving cars from the movie I, Robot, and virtual assistants like Iron Man’s own JARVIS are very much part of our modern life today.
We have virtual assistants, chatbots, translators, human emotion interpreters, autonomous cars, and, heck, even self-driving helicopters or ‘air taxis’ today. These cutting-edge innovations would not have been possible without the advancements in machine learning and artificial intelligence.
AI projects not only belong to high-budget, advanced laboratories in tall tech buildings. You can get your hands dirty with some basic AI skills as well. Start small, as they say. Here we have a list of Data Science Projects that you can work on to proudly display in your portfolio.
Why Should You do AI-Based Projects?
For data scientists everywhere, AI, machine learning, and deep learning are some of the coolest parts of their skill sets, or they eventually aspire to achieve these skills. Let me give you an instance to think about, and maybe you can relate to it. Say you have just become an advanced Python programmer and would like to implement your learning in the areas of machine learning and artificial intelligence. There is no better way to spread your wings than to work on small projects that demonstrate your ability.
The demand for professionals with AI skills is at an all-time high. Artificial intelligence comprises Machine Learning, Deep Learning, NLP (Natural Language Processing), Robotics, and Fuzzy Logic.
Machine Learning is at the base of the pyramid that stacks up to form AI. Machine learning and artificial intelligence have become 75% more popular among the most demanded technologies in the past four years. It is a multi-billion dollar industry at the moment and shows no signs of declining. This is a great time for you to hop on the bandwagon.
Basic Level Artificial Intelligence Project Ideas
1. AI Chatbot
Customers of various websites and applications no longer wish to call customer care service and stay on hold for hours. They require immediate services available at the click of a button, and AI can serve this purpose successfully. Most websites today have chatbots built-in that allows the user or customer to interact with and address their issues.
Chatbots are built using NLP (Natural Language Processing). NLP helps break down information into machine language. This way, it understands human interactions, be it text or audio. With the help of tools that have already been trained to recognize text or speech, the chatbot can be built to become a well-responsive tool.
2. Stock Prediction
People have long invested in the stock market and are constantly looking for predictions so that they can best make their decisions about investment. For years, traders have taken to Fundamental Analysis or Technical analysis that looks into the company’s past performance and credibility or focuses on the stock trends alone, respectively, to predict the increase or decrease in interested stock prices.
Enter machine learning.
Stock Prediction is a forecasting tool that is applied to predicting stock trends. This requires months of data on stock movement and is a popular Data Analytics Project among machine learning beginners. It involves Artificial Neural Networks (ANN) as well as Genetic Algorithms (GA) that use feed-forward networks to predict stock trends. Since your predictions can be validated sooner rather than later, it comes out as a good AI project, to begin with, so that you may validate your predictions without delay.
3. Handwriting Recognition
The digitalization movement has reached almost every sector on the face of the earth. However, there still exist some offices and workflows that do their work by hand and digitize it eventually. An AI project to help with this process is a handwriting recognition system. Another application of this system is to convert handwritten mathematical symbols and equations to digital format.
Since the handwriting of various people differs in that style, the curves and sizes of the letters vary widely. Convolutional Neural Network (CNN) can be utilized here to extract the visual features of a line of text that further be recognized by Recurrent Neural Network to construct the characters. This AI project is an excellent one to showcase your ability to use multiple deep-learning methods.
4. Music Recommendation App
Have you ever tried to google by humming a song? How about using Shazam to recognize a song? This is the future. We can now use AI to recognize and recommend music to the user. An application that recommends music can be built using techniques such as Collaborative Filtering that helps recommend music to users based on the tastes of similar users. This similarity is also dug up by analyzing the user's historical data, their likes, and dislikes in music.
When using AI for music recommendation, ANN can be employed to recognize the genre or the singer or the metadata of the audio signal features from the dataset provided to train with successive network training.
5. Lane Line Detection
Here’s a great AI project for you. Drive your car around your neighborhood in different lightings and weather conditions, and collect data by recording the lanes from your smartphone. With the extracted footage, you can extract image frames. You have your own dataset for lane line detection.
There are multiple methods to detect lanes that have varied levels of accuracy and performance. Most AI projects begin with processing the images found using feature-based or model-based detection methods. Or one can simply use the keras-vis library, which is a neural network visualization toolkit for keras. Finally, implement CNN that utilizes convolutional layers in tandem with batch normalization and ReLU activation, as well as deconvolutional layers. This way, you have yourself a fully Convolutional Neural Network (CNN). This AI project is definitely a fun one.
Advanced Level Artificial Intelligence Project Ideas
1. Web Pattern Navigation Recognition
When you use a website, your movements and, how you navigate through the websites, the clicks and tabs you open are all stored in the back-end logs. These logs are so full of data that it is visually impossible to make head or tail of it. Using web-pattern navigation profiling or automatic pattern discovery (APD), applications can detect anything from grammatical errors to cybersecurity threats. It even helps corporations and governments detect criminal and civil turmoil in social media and the internet at large.
To perform web pattern recognition, one can use classification, clustering, and regression algorithms. These algorithms help label the predefined features of the data, split them into clusters based on similarities, and predict unknown patterns based on the known relationships between features. Some examples include the Decision Tree algorithm, Naive Bayes, Support Vector Machines, k-means clustering, Mean Shift, etc.
2. Pneumonia Detection
Pneumonia is a deadly affliction that can be treated if detected early. Since the disease makes camp in the lungs, a chest X-ray is one of the ways to diagnose someone of Pneumonia. Today, an AI can read the X-ray to detect if there are signs of pneumonia. Deep learning methods like the CNN architecture and Transfer Learning (TL).
As any visual input-based machine learning flows, the first step is image pre-processing. Then split the dataset into train and test. Next, apply VGG16, which is a CNN architecture containing a visual database often used in visual object recognition systems. It will help us categorize if the images fed can be classified as having pneumonia or not. Furthermore, after implementing convolutional layers, flattening the layers, and combining them with VGG16 output, we get a model which can be later compiled using adam optimizer. Finally, use the predict() function to check the output of the model.
The applications of this AI project are very significant in the medical field and can even be integrated into a mobile application for more handy use.
3. AI-Assisted Surgery Robot
Artificial Intelligence is now an integral part of surgical practices around the world. Robots powered by AI can now perform intricate tasks with high accuracy, which is extremely useful in hospitals. Deep learning can be implemented by training the bot with footage from the surgeon’s procedures. Such data coupled with complex algorithms can help in the Operating Room (OR) to make accurate, submillimetric procedures that can prove difficult for surgeons. Some applications include hair transplant surgeries, gastrointestinal procedures, as well as cancer detection.
4. AI-powered Companion Robots in Education
Gen-Z and Gen-Alpha kids are more exposed to advanced technology than the generations preceding them. The age of technology has brought the tiny tots closer to artificial intelligence than one might expect. Children’s education can now be accompanied by AI-powered robots. No, I’m not talking about Megan or Chucky. The real one is that actually helps the children by catering to their individual interests and pace of learning. These companion robots can help with the shortage of teachers by gauging the child’s learning style and customizing the teaching approach to better the student’s performance.
Most companion robots are built using ANN and NLP but are also integrated with speech-generation algorithms, speech synthesis technology, facial recognition technology, machine perception, motor control, and more to make the robot as responsive and child-friendly as possible.
5. Automotive Radar for Full Autonomous Vehicles
Fully Autonomous Vehicles, or self-driving cars, need input from around the car to successfully navigate the route without running into obstacles. They need to detect not only moving cars but also stationary objects surrounding them. This requires more than just camera-based perception, which can get clouded due to bad weather conditions or poor lighting. The additional radar available onboard may always fall short in differentiating between moving and stationary objects, especially the size and nature of the obstacle itself.
The radar only reads the obstacle as a cluster but does not give it a shape or form. It could be a person, a car by the side of the road, or even just the railing. In order to get around this issue, one can use AI techniques like Deep Neural Networks (DNN). Training the DNN with data from a combination of radar and lidar (3-D portrayal of objects using laser pulses) will help the AI learn the difference between various objects and instruct the control systems of the car to make better decisions. This way, self-driving cars can one day be trusted on the roads.
Interesting AI Projects in Python
Here are some AI projects that you can try for yourself.
1. Face Recognition
Facial recognition is a popular identification and verification system, especially in mobile applications. It has become a reliable biometric security option ranging from corporations to Border and Immigration. Machine learning methods can be used to extract image features which are then put through the following processes using CNN and Deep Autoencoders Network - face detection, alignment, extraction, and finally, face recognition.
2. Image Colorization
A lot of monochromatic footage from years ago has been digitally colorized. A great example is the footage from World War II with impeccable color information added to it. However, performing image colorization requires mapping and can be achieved through CNN.
3. Human Pose Estimation
Human pose estimation works by visually recognizing the orientation of the human body in a graphical format. It can help classify body poses in 2-D as well as 3-D. With this AI project, autonomous cars can predict the movement of pedestrians.
There are various libraries that you can import that help estimate the pose such as OpenPose, PoseDetection (TensorFlow package), and HRNet. Such AI projects can have a complicated approach that involves an encoder-decoder architecture coupled with heatmaps, to find the key-point likelihood from the heatmap image. However, this AI project can look very impressive once you’ve added it to your arsenal of projects.
You have a whole lot of AI projects to try out for yourself. Practicing with your own AI projects and prediction models will help your understanding of the concepts and even ace interviews.
Just a few of these AI projects will be enough to spice up your portfolio and land you attractive job offers. Find some cool Python Data Science Projects in the StrataScratch platform. You can also practice your python and SQL skills by solving interview questions of various levels of difficulty on the platform. Alright, time to roll up your sleeves. Good luck!