A career in Artificial Intelligence (AI) seems bright with the recent developments in this field.
Almost all the sectors are leveraging AI for their benefit, from IT, manufacturing, and automobile to defense, finance, and content creation,
So, if you want to build a career in AI, there can never be a better time to start than now. Since hands-on experience is the best way to learn a skill, you can do different projects to learn AI and related skills like programming and using tools and technologies.
It will teach you how AI can help people and businesses in real-time and help you gain knowledge in this sector to advance your career in AI. And for this, it would be highly beneficial if you have knowledge of skills like:
- Programming languages such as Python, R, Java, MATLAB, and Perl
- Machine learning algorithms like linear regression, logistic regression, Naïve Bayes, K-means, KNN, SVM, and decision trees
- Data analysis basics and tools like Apache Spark
- Artificial neural networks (ANN) that can mimic the brain functions of humans to solve problems in apps for handwriting, face, and pattern recognition
- Convulsion Neural network (CNN) basics
- Unix-based tools like Sort, AWK, and regular expressions.
Now, let’s quickly discover some of the interesting AI projects.
Basic AI Projects
Handwritten Digit Recognition
Aim: To build a system that can recognize handwritten digits with the help of artificial neural networks
Problem: Digits and characters written by humans consist of various shapes, sizes, curves, and styles, not exactly the same for two people. So, converting written characters or digits into a digital format was challenging in the past for computers. They also used to struggle at interpreting text on paper-based documents.
Although digitization is being rapidly adopted in almost every sector, certain areas still require paperwork. This is why we need technology to make this process easy for computers so that they can recognize human writings on paper.
Solution: Using artificial neural networks make it possible to build a handwritten digit recognition system to precisely interpret the digits that a person draws. For this, a convolution neural network (CNN) is utilized for recognizing digits on a paper. This network has a HASYv2 dataset comprising 168,000 images from 369 different classifications.
Application: Apart from papers, a handwritten digit recognition system can read mathematical symbols and handwriting styles from photos, touchscreen devices, and other sources. This software has various applications like bank cheque authentication, reading filled forms, and taking quick notes.
Lane Line Detection
Aim: To create a system that can connect with self-driven vehicles and line-following robots to help them detect lane lines on a road in real-time.
Problem: Undoubtedly, autonomous vehicles are innovative technologies that use Deep Learning techniques and algorithms. They have created new opportunities in the automobile sector and reduced the need for a human driver.
However, if the machine driving a self-driving car is not appropriately trained, it may cause risks and accidents on the road. While training the machine, one of the steps is to make the system learn how to detect lanes on the road so it does not get in another lane or collide with other vehicles.
Solution: To solve this problem, build a system using the concepts of Computer Vision in Python. It will help the autonomous vehicles detect lane lines properly and ensure it runs on the road where it should be, without risking others.
You can use the OpenCV library – an optimized library that focuses on real-time usage like this one to detect lane lines. The library includes Java, Python, and C++ interfaces that support Windows, macOS, Linux, Android, and iOS platforms.
Furthermore, it’s imperative to find the markings on a lane’s both sides. You can use computer vision techniques in Python to find the road lanes where self-driving cars should run. You must also find the white marking on a lane and mask the rest of the objects with frame masking and NumPy arrays. Nest, the Hough line transformation is applied to finally detect the lane lines. In addition, you can use other computer vision methods such as color thresholding to identify the lane lines.
Application: Lane line detection is used in real-time by autonomous vehicles like cars and line-following robots. It’s also helpful in the gaming industry for racing cars.
Aim: To build an AI system using convolution neural networks (CNNs) and Python that can detect pneumonia from a patient’s X-ray images
Problem: Pneumonia is still a threat, claiming lives in many countries. The issue is that X-ray images are taken to detect the diseases like pneumonia, cancer, tumor, etc., in general, which can provide low visibility and make the assessment inefficient. But if proper treatment is followed, the mortality can be significantly reduced.
Furthermore, the position, shape, and size of pneumonia can differ at a significant level, with its target contour becoming largely vague. It increases detection and accuracy issues. This brings us to develop a technology that can identify pneumonia early with optimum accuracy to give proper treatment and save lives.
Solution: The software solution will be trained with massive details on pneumonia or other diseases. When users share their health-related issues and symptoms, the software can process the information and check it against its database for possibilities related to those details. It can use data mining to provide the most precise disease corresponding to the patient’s details.
This way, a patient’s disease can be detected, and they can get proper treatment. And to design the software, you must determine the most efficient CNN model analytically and comparatively to achieve pneumonia detection from X-ray images using feature extraction. Next comes presenting the different models with their classifiers to propose the most suitable classifier and evaluating the best CNN model to check its performance.
Application: This AI project is beneficial for the healthcare domain to detect diseases like pneumonia, heart ailments, etc., and provide medical consultation to the patients.
Aim: To build a chatbot using Python to embed it in a website or application
Problem: Consumers need excellent service when they use an application or website. If they have a query they cannot find the answer to, they may lose interest in the app. So, if you are building a website or application, you must offer the best quality service to your users so you don’t lose them and impact your bottom line.
Solution: A chatbot is an application that can enable automatic conversation between bots (AI) and a human via text or speech like Alexa. It’s available 24/7 to help users with their queries, navigate them, personalize user experience, boost sales, and provide deeper insights into customer behavior and needs to help you shape your products and services.
For this AI project, you can use a simple version of a chatbot that you can find on many websites. Identify their basic structure to start building a similar one. Once you have finished a simple chatbot, you can move to advanced ones.
To create a chatbot, AI concepts like Natural Language Processing (NLP) are used to enable algorithms and computers to comprehend human interactions through various languages and process those data. It breaks down audio signals and human text and then analyzes and converts the data in a machine-understandable language. You will also require different pre-trained tools, packages, and speech recognition tools to create an intelligent and responsive chatbot.
Application: Chatbots are highly useful in the corporate sector for customer service, IT helpdesk, sales, marketing, and HR. Industries from eCommerce, Edtech, and real estate to finance and tourism use chatbots. Top brands like Amazon (Alexa), Spotify, Marriott International, Pizza Hut, Mastercard, and more leverage chatbots.
Aim: To build a recommendation system for customers for products, videos and music streaming, and more, with the help of ANN, data mining, machine learning, and programming.
Problem: Competition is high across all domains, whether it’s eCommerce or entertainment. And to stand out, you must cover extra miles. If you offer something that your target customer is looking for but don’t have the measures to guide them to your shop or recommend your offerings, you leave a lot of cash on the table.
Solution: Using a recommendation system can draw more visitors to your site or application effectively. You might have observed that eCommerce platforms like Amazon offer product recommendations that you have searched for somewhere on the internet. When you open your Facebook or Instagram, you see similar products. This is how a recommendation system works.
To build this system, you require browsing history, customer behavior, and implicit data. Data mining and machine learning skills are necessary to produce the most suitable product recommendations based on customers’ interests. And you will also need to program in R, Java, or Python and leverage artificial neural networks.
Application: Recommendation systems find huge applications in eCommerce stores like Amazon, eBay, video streaming services like Netflix and YouTube, music streaming services like Spotify, and more. It helps increase product reach, number of leads and customers, visibility across various channels, and overall profitability.
Intermediate AI Projects
Aim: To build a fire detection system using CNN for tasks related to computer vision and image classification
Problem: Fires in residential and commercial buildings are dangerous. If the fire is not detected on time, it can lead to massive loss of life and property. Wildfires are becoming more frequent; therefore, regular monitoring is needed to preserve wildlife and natural resources.
Solution: Building a system that can detect fire indoors and outdoors at an early stage and with its exact location can help extinguish it before it can cause any harm. The fire detection system is improved through a surveillance camera.
For this, AI techniques like CNN and computer vision and tools like OpenCV are used. It needs sophisticated image processing and cloud computing. The system can be made to analyze images from video cameras for visible light and infrared. It must also identify smoke, differentiate it from fog, and alert people quickly.
Application: AI-powered fire detection can be used to detect forest fires to preserve natural resources, flora, and fauna and in homes and corporate buildings.
Voice-Based Virtual Assistant
Aim: To build an application with voice capabilities to assist users
Problem: The web is vast with many products and services that customers may feel overwhelmed. In addition, people are busy and need help in various fields, even for their day-to-day tasks.
Solution: Today, voice-based virtual assistants are in demand to simplify users’ lives. People can use these applications like Alexa and Siri for entertainment purposes, search online products and services, and perform everyday tasks for better productivity.
To build this system, NLP is used to understand human language. The system will hear the voice, convert it into machine language, and save the commands in its database. It will also identify users’ intent to perform the task accordingly and may use text-to-speech or speech-to-text tools.
Application: Voice-based virtual assistants are used to find relevant items on the internet, play music, movies, and videos for entertainment, set reminders, write quick notes, activate and deactivate home appliances, and more.
Aim: To create a system that can check a document for plagiarism or duplication using AI
Problem: Content duplication is a disease, which must be monitored and eradicated. For businesses, it leads to reputation damage and bad search engine rankings. In fact, people may also get penalized for plagiarism, owing to copyrights. Hence, there is a need to identify plagiarized content for businesses and educational institutions.
Solution: AI concepts are used to build a plagiarism checking tool to detect duplication in a document. In this project, Python Flask or text mining can be used to detect plagiarism using a vector database called Pinecone. It can also show the plagiarism percentage.
Application: Plagiarism checker has many benefits for content creators, bloggers, editors, publishers, writers, freelancers, and educators. They can use it to check if somebody has stolen their work and using it, while editors can analyze a write-up submitted by a writer and identify whether it’s unique or copied from somewhere.
Facial Emotion Detection
Aim: To build an application that can predict or identify human emotions through facial features using AI
Problem: Understanding human emotions is challenging. There has been a lot of research for decades to comprehend facial emotion. Before the advent of AI, the results were all over the place.
Solution: AI can help analyze human emotion through face using the concepts like Deep Learning and CNN. Deep learning can be used to build the software to identify facial expressions and interpret them by detecting core emotions in humans in real-time like happiness, sadness, fear, anger, surprise, disgust, neutral, etc.
The system will be made capable of extracting facial features and classifying expressions. CNN can do this and will also discriminate between bad and good emotions to detect an individual’s behavior and thinking patterns.
Application: Facial emotion detection systems can be used by bots to improve human interaction and provide suitable help to users. They can also help children with autism, people with blindness, monitor attention signs for driver safety, and more.
Aim: To build a translator application using artificial intelligence
Problem: There are thousands of languages spoken in the world. Although English is a global language, not everyone understands it in every part of the world. And if you want to conduct business with someone from other countries who speaks a language you don’t understand, it’s problematic. Similarly, if you travel to other countries, you can face similar problems.
Solution: If you can translate what others are saying or have written, it will help you connect with them deeply. For this, you can use a translator such as Google Translate. However, you can build your own app from starch using AI.
For this, you can utilize NLP and transformer models. A transformer will extract features from a sentence to determine each word and its significance that can make the complete sense of a sentence. It will encode and decode words from end to end. To do this, loading a pre-trained Python-based transformer model will help you. You can also use the GluonNLP library and then load and test the datasets.
Application: The translator app is used for translating different languages for purposes like business, travel, blogging, and more.
Advanced AI Projects
Aim: To build software using AI that can skim through a lot of resumes and help users choose the ideal one
Problem: In recruitments, professionals spend a vast time going through a lot of resumes, one by one, manually to find suitable candidates for a job post. It’s time-consuming and inefficient. Although it can be automated through keyword matching, it has many disadvantages. Candidates who know this procedure will add many more keywords to get shortlisted, while others will be rejected even if they have the required skills.
Solution: Skimming through a large number of resumes and finding the right fit for a job role can be automated using a resume parser. It will help you do it efficiently, saving time and effort while allowing you to choose candidates with the required skills.
AI and ML can help you build the application to choose a suitable candidate while filtering out the rest. To do this, you can utilize the Resume Dataset on Kaggle with two columns – resume info and job title. You can also use NLTK – a Python-based library – to build clustering algorithms to match skills.
Application: A resume parser is used for the recruitment process and can be used by businesses and education institutions.
Face Recognition App
Aim: To build an app with face recognition capability using ANN, CNN, ML, and deep learning
Problem: Identity theft issues are grave with the increasing cybersecurity risks that can infiltrate systems and data. It may cause privacy issues, data leaks, and reputational damage to people and businesses.
Solution: Biometrics like facial features are unique, so organizations and individuals can use them to protect their systems and data. Facial recognition systems can help verify a user, ensuring only the authorized and authenticated users can access a system, network, facility, or data.
You need advanced ML algorithms, mathematical functions, and 3D image processing and recognition techniques to build this solution.
Application: It’s used in smartphones and other devices as a security lock and organizational facilities and systems to ensure data privacy and security. It’s also used by Identity and Access Management (IAM) providers, the defense sector, and more.
Aim: To create video games using AI concepts
Problem: Video gaming industry is expanding, and gamers are becoming more advanced. Hence, there is a constant need to evolve and provide interesting games that stand out while you continue driving your sales.
Solution: AI concepts are used to create various gaming applications like chess, snake games, racing cars, procedural games, and more. It can use many skills like chatbots, speech recognition, NLP, image processing, data mining, CNN, machine learning, and many more to create a realistic video game.
Application: AI is used to create various video games like AlphaGo, Deep Blue, FEAR, Halo, and more.
Aim: To create software that can predict sales for businesses
Problem: Businesses dealing with many products face difficulties managing and keeping track of every product’s sales figure. They also find trouble tracing the stocks and making the sold-out products available again. AS a result, they may fail at supplying products at the right to users, which degrades customer experience.
Solution: Building a sales predictor tool can help you predict the average sales figure daily, weekly, or monthly. This way, you can understand how your products perform and stock more items on time to meet the customer demands.
To do this, you may utilize skills like machine learning algorithms, data analysis, Big Data, and more to enable the software to predict sales accurately.
Application: It’s used by eCommerce stores, retailers, distributors, and other businesses dealing with massive products.
Aim: To create a software solution that can automate certain tasks for productivity
Problem: Repeated, manual work is time-consuming. These are not only tedious but also take away productivity. Hence, a system needs to be built that can automate different tasks such as scheduling calls, taking attendance, autocorrection, processing transactions, and more.
Solution: Using AI lets you build software that can automate such tasks to help improve user productivity and dedicate time to more critical tasks. It can also be made to deliver in-time notifications so you can do tasks on time. And building this system requires skills like NLP, facial recognition, computer vision, and more.
Application: Automation using AI is widely used to build productivity tools for businesses of all sizes and in various sectors from banking, finance, healthcare, education, and manufacturing.
I hope you find these AI projects interesting to work with and expand your knowledge in artificial intelligence and other related concepts like data science, machine learning, NLP, etc. It will also help you sharpen your skills in programming and using tools and technologies in the projects.
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