A career in Artificial Intelligence (AI) looks bright with recent advances in the field.
Almost every industry is leveraging AI to its advantage, from IT, manufacturing and automotive to defense, finance and content creation,
So if you want to carve out 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 undertake different projects to learn AI and related skills such as programming and the use of 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 very beneficial for you to have knowledge of skills such as.
- Programming languages such as Python, R, Java, MATLAB and Perl
- Machine learning algorithms such as linear regression, logistic regression, Naive Bayes, K-means, KNN, SVM and decision trees
- Basic concepts of data analysis and tools such as Apache Spark
- Artificial neural networks (ANNs) that can mimic human brain functions to solve problems in handwriting, face, and pattern recognition applications
- Basics of Convolutional Neural Networks (CNN)
- Unix-based tools such as Sort, AWK and regular expressions.
Now, let’s quickly discover some of the most interesting AI projects.
Basic AI projects
1. Handwritten digit recognition
Objective: 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, and are not exactly the same for two people. Therefore, converting the written characters or digits into a digital format was challenging for computers in the past. They also used to have difficulty interpreting text from paper documents.
Although digitization is being rapidly adopted in almost all sectors, certain areas still require paperwork. That’s why we need technology to make this process easier for computers so that they can recognize human writing on paper.
Solution: The use of artificial neural networks makes it possible to build a handwritten digit recognition system to accurately interpret digits drawn by a person. For this purpose, a convolution neural network (CNN) is used to recognize the digits on a piece of paper. This network relies on a HASYv2 dataset consisting of 168,000 images of 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 a variety of applications, such as authenticating bank checks, reading filled-out forms and taking quick notes.
2. Lane line detection
Objective: Create a system that can interface with autonomous 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 automotive industry and have reduced the need for a human driver.
However, if the machine driving an autonomous car is not properly trained, it can cause risks and accidents on the road. When training the machine, one of the steps is to make the system learn to detect the lanes of the road so as not to invade another lane or collide with other vehicles.
Solution: To solve this problem, build a system using computer vision concepts in Python. It will help autonomous vehicles correctly detect lane lines and ensure that it drives on the road where it should, without endangering others.
You can use the OpenCV library, an optimized library that focuses on real-time use like this to detect lane lines. The library includes Java, Python and C interfaces that are compatible with Windows, macOS, Linux, Android and iOS platforms.
In addition, it is essential to find the markings on both sides of a lane. You can use computer vision techniques in Python to find the lanes on the road where self-driving cars should drive. You must also find the white marking of a lane and mask the rest of the objects with frame masking and NumPy arrays. Nest, Hough’s line transform is applied to finally detect the lane lines. In addition, you can use other computer vision methods such as color thresholding to identify lane lines.
Application: Lane line detection is used in real time by autonomous vehicles such as cars and line-following robots. It is also useful in the gaming industry for racing cars.
3. Detection of pneumonia
Goal: 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 remains a threat that claims lives in many countries. The problem is that X-ray images are taken to detect diseases such as pneumonia, cancer, tumors, etc., in general, which can provide low visibility and make the evaluation ineffective. But if proper treatment is followed, mortality can be greatly reduced.
In addition, the position, shape and size of the pneumonia can differ at a significant level, thus making its target contour largely inaccurate. This increases detection and accuracy problems. This leads us to develop technology that can identify pneumonia early with optimal accuracy to administer appropriate treatment and save lives.
Solution: The software solution will be trained with massive details about pneumonia or other diseases. When users share their health problems and symptoms, the software will be able to process the information and check it against its database for possibilities related to those details. It can use data mining to provide the most accurate disease corresponding to the patient’s details.
In this way, a patient’s disease can be detected and the patient can receive the appropriate treatment. And to design the software, it should determine the most effective CNN model analytically and comparatively to achieve the detection of pneumonia from X-ray images by feature extraction. In the following, the different models with their classifiers are presented to propose the most suitable classifier and the best CNN model is evaluated to check its performance.
Application: This AI project is beneficial for healthcare domain to detect diseases like pneumonia, cardiac ailments, etc., and provide medical consultation to patients.
Objective: Build a chatbot using Python to embed in a web page or application.
Problem: Consumers need excellent service when using an application or website. If they have a query for which they can’t find an answer, they may lose interest in the application. Therefore, if you are creating a website or an application, you need to provide the best quality service to your users so that you don’t lose them and don’t affect your bottom line.
Solution: A chatbot is an application that enables automated conversation between bots (AI) and a human via text or voice such as Alexa. It is available 24/7 to help users with their queries, guide them, personalize the user experience, drive 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 its basic structure to start building a similar one. Once you have finished a simple chatbot, you can move on to advanced ones.
To create a chatbot, you use AI concepts such as Natural Language Processing (NLP), which allows algorithms and computers to understand human interactions through various languages and process that data. It breaks down audio signals and human text and then parses and converts the data into machine-understandable language. You will also need different pre-trained tools, packages and speech recognition tools to create an intelligent and responsive chatbot.
Application: Chatbots are very useful in the corporate sector for customer service, IT helpdesk, sales, marketing and human resources. Industries ranging from e-commerce, education technology and real estate to finance and tourism use chatbots. Major brands such as Amazon (Alexa), Spotify, Marriott International, Pizza Hut, Mastercard and others leverage chatbots.
5. Recommendation system
Goal: Build a customer recommendation system for streaming products, videos, music, and more, with the help of ANN, data mining, machine learning, and programming.
Problem: Competition is high across the board, whether it’s e-commerce or entertainment. And to stand out, you must go the extra miles. If you offer something your target customer is looking for but don’t have the metrics to guide them to your store or recommend your offerings, you’re leaving a lot of money on the table.
Solution: Using a referral system can effectively attract more visitors to your site or application. You may have noticed that e-commerce platforms like Amazon offer recommendations for products you’ve searched for somewhere online. When you open your Facebook or Instagram, you see similar products. This is how a recommendation system works.
To build this system, you need browsing history, customer behavior and implicit data. You need data mining and machine learning skills to craft the most appropriate product recommendations based on customer interests. And you will also need to program in R, Java or Python and leverage artificial neural networks.
Application: Recommender systems find huge applications in e-commerce stores like Amazon, eBay, video streaming services like Netflix and YouTube, music streaming services like Spotify, etc. They help increase product reach, number of leads and customers, multi-channel visibility and overall profitability.
I hope you find these AI projects interesting to work on and expand your knowledge in artificial intelligence and other related concepts such as data science, machine learning, NLP, etc. They will also help you to hone your skills in programming and in the use of tools and technologies in the projects.