Artificial Intelligence is progressing rapidly, from chatbots to self-driving cars. Due to the numerous benefits and growth offered by AI, many industries started looking for AI-powered applications. As a result, there is a huge demand for Artificial Intelligence (AI) careers, but there is a significant shortage of sharp minds with the necessary skills to fill these positions. The best approach to overcome this shortage is to gain these necessary skills by working on real-world AI applications. This article lists 18 different AI projects that take you through simple AI projects to advanced artificial intelligence projects. By working on these projects, you will be able to grasp various techniques such as bag-of-words, random forest, LempelZiv (LZ) algorithm, Markov Model (MM), Neural Networks (NNs), Bayesian Networks, Association rules, Word2Vec approach, k-nearest neighbor classifier, Bonferroni, FDR corrections, and so on.
Artificial Intelligence is advancing throughout the world. According to a study by Creative Strategies, 97% of mobile users are using AI-enabled voice assistance. It is hard to find a society that doesn’t use AI techniques. This technique brings numerous benefits in a multitude of ways. It includes decision-making capabilities, diagnosis generation, identifying the relationship between causes and consequences, forecasting events, controlling devices such as smart sensors, mechanical arms, etc.
Artificial Intelligence refers to the intelligent agents in machines capable of executing a task that requires human intelligence. The goal of these intelligent agents includes learning, reasoning, problem-solving, and perception. AI includes many theories, methods, and technologies. It consists of many subfields, such as machine learning, neural network, deep learning, cognitive computing, computer vision, and natural language processing.
In this article, we will take you through 18 artificial intelligence project ideas that advance from simple AI projects to advanced level AI projects. These real-world applications will help you gain the necessary skills in AI within a short span.
Table Of Contents
- Why should you do an AI-based Project?
- Interesting AI projects in Python
- Predicting user’s next location
- Detecting YouTube comment spam
- Identifying the genre of a song
- Shock front classification
- Simple AI projects for beginners
- Predicting bird species
- Identifying handwritten mathematical symbols
- Scotch whisky classification
- Investigate Enron
- AI in business
- Automatic system for detecting trends in fashion
- Web pattern navigation profiling for online marketing
- AI in everyday life
- Unlocking phone using faceID
- Gmail’s smart reply
- Uber’s ride-sharing app
- Google AI projects
- Forecasting earthquake aftershock locations
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Why Should You Do An AI-based Project?
Industries across the world are demanding for AI-based software applications like never before. According to a study by Statista, industries will grow to $120 billion by 2025 through AI applications. No businesses are ready to ignore this opportunity. Companies who have implemented AI-based chatbots have experienced great growth in their businesses.
This spike in demand for AI technologies has laid the foundation for many AI-powered software companies. And, software companies started looking for smart minds and intelligent ideas to meet the demand. As per the stats from McKinsey&Company, by 2030, above 375 million people have to adapt to AI-based careers to meet the future demand. Doing real-world AI-based projects is the best approach to gain the necessary skills, and it will help you get jobs in these sectors.
Interesting AI Projects In Python
Predicting user’s next location
Predicting the user’s most probable next location (next summer vacation, holidays, etc.) becomes an important requirement to make decisions for future services. These services include healthcare applications, network management, travel management, and so on. Working on this AI project model will help you to understand the LempelZiv (LZ) algorithm, Markov Model (MM), Neural Networks (NNs), Bayesian Networks, and Association rules. https://dl.acm.org/doi/abs/10.1145/2676552.2676557?download=true
Detecting YouTube comment spam
The popularity of YouTube not only attracted genuine viewers but spammers as well. As a result, there is an increase in unwanted spam videos and comments. Here comes the importance of an AI-based YouTube spam comment detection model. In this AI project, you will be focusing on text and words and classify internet comments as spam or not spam. The spam detection model can be accomplished by using bag-of-words and random forest techniques. You can also predict positive and negative reviews with the Word2Vec approach and the k-nearest neighbour classifier in addition to spam detection.
Identifying the genre of a song
In this AI project, you will be identifying the genre of a song using an artificial neural network. You will be using Librosa (python library) to extract features from the song and Mel-frequency cepstral coefficients (MFCC) to detect the music genre.
Shock front classification
This AI project detects shock fronts in computational fluid mechanics (CFD) simulations. The presence of shock results in additional complexities in fluid mechanics, and hence, it is necessary to detect and handle shock fronts to deal with fluid mechanics problems. Here, you will be using supervised algorithms for classification such as classification trees (RPART), linear discriminant analysis (LDA), naive Bayes (NB), support vector machines (SVM), and random forests (RF).
Simple AI Projects For Beginners
Predicting bird species
Birds are ecological indicators, and they respond quickly to environmental changes. Hence, it is important to classify birds to understand the problems in ecology. Domain experts can classify birds manually, but manual classification has become a tedious and time-consuming process due to the tremendous increase in amounts of data. Here comes the importance of artificial intelligence-based classification. This classification-based AI project can be approached in two ways. If you are a beginner, you can use a random forest to predict bird species. If you are looking for an intermediate level, you can use a convolution neural network. https://www.oreilly.com/library/view/python-artificial-intelligence/9781789539462/c58c97ba-d076-4248-901c-0ca48de0108c.xhtml
Identifying handwritten mathematical symbols
In this Artificial Intelligence project, you will be using a convolution neural network (CNN) to detect handwritten mathematical symbols. The HASYv2 dataset is the input to the neural network; it contains 168,000 images from 369 different classes.
Scotch whisky classification
Scotch whiskey is famous for its distinct flavours. In this simple AI project, you will classify scotch whiskeys based on their flavour characteristics. Here, we will use datasets of scotch whiskeys from several distilleries and cluster it based on the flavours. Please check the below link for datasets.
Enron is one of the largest energy companies in America that collapsed overnight. This AI project investigates Enron fraud activities with the help of the emails sent by their former senior executives. It has 500 thousand emails from their former employees. Check the below link for the Enron database.
AI In Business
Automatic system for detecting trends in fashion
Coolhunter has gained significant importance in the fashion world. They take advantage of social media platforms to understand new trends in fashion. But, due to irrelevant information, it becomes a challenging task to predict fashion trends. This AI-based project filters relevant information from the irrelevant one.
Web pattern navigation profiling for online marketing campaigns
Each time when users search for information on the internet, they leave an invisible blueprint of their preferences. These preferences are recorded based on their browsing behavior in a specific sequence of domains. Here, segments of user groups are created based on their browsing habit or social media opinions.
In this Artificial Intelligence project, you will learn a new perspective in collecting user preferences. Here, different navigation profiles are extracted based on the consecutive sequence of domain visiting order and the route followed within a certain socio-demographic profile. Here, you will define an algorithm to extract frequent contiguous sequences and also use Bonferroni and FDR corrections to retrieve socio-demographic characteristics.
Food attribute classification using a multi-scale convolutional network
This AI-based project classifies the diverse array of food based on cuisine and its flavors. Here, we create a deep learning model based on a multi-scale convolutional network. The food attribute dataset – Yummly48k – is taken from the website Yummly. In addition to the multi-scale convolutional network, it also uses Negative Log-Likelihood (NLL) for the model creation.
AI In Everyday Life
Maintaining a healthy lifestyle plays a key role in preventing the cause of chronic diseases. The right amount of nutrition is a must to maintain a healthy lifestyle, but due to a poor diet plan, a major chunk of the population is suffering from undernutrition.
Diet4You is an intelligent decision support system (IDSS) that uses different techniques to tailor a personalized menu planner. It not only considers the prescription provided by the nutritionist but also considers various other factors such as the nutritional guidelines that are to be followed, the person’s characteristics, health status, habits, food preferences, allergies, and so on. This AI project combines advanced techniques such as Knowledge Engineering, Case-Based Reasoning (CBR), and Data Analysis. Diet4You consists of two main modules: NPG module – tailor a nutrition plan for a specific person – and PMP module – a nutrition plan for a specific period.
Unlocking phone using faceID
It is one artificial intelligence project that uses face biometrics to unlock a phone. Using deep learning, the AI application can extract image features. It mainly uses two types of neural networks: Convolution neural networks and Deep autoencoders network. And, it is a four-step process. They are- face detection, face alignment, face extraction, and face recognition.
Gmail’s Smart Reply
Gmail’s smart reply uses a machine-learning algorithm to suggest replies to email. It is based on a novel thinking hierarchy where each hierarchical model can learn, remember, and recognize a sequential pattern. While responding, it considers whether it is a positive gesture or a negative gesture. It uses technologies such as long-short-term-memory (LSTM) recurrent neural networks and semantics.
Uber’s ride-sharing app
Uber is one of the biggest cab service providers. It uses artificial intelligence to make predictions about market demand, provide better customer experience, find the best route for drivers, detect fraud, etc. Uber AI uses various techniques such as forecasting, demand modeling, dynamic pricing, and so on.
Google AI Projects
Forecasting earthquake aftershock locations with AI-assisted science
Earthquakes cause massive destruction in the entire world. It initially occurs as a mainshock and is then followed by a set of aftershocks. The timing and size of aftershocks can be identified using empirical laws, but forecasting the locations remains a challenging part. This Google AI project applies deep learning to identify the location where the aftershock might occur. The project uses the information of 118 major earthquakes reported around the world. Here, it uses a neural network to analyze the static stress change of mainshock and aftershock locations.
XTREME is a pre-trained multilingual model that includes 40 typologically diverse languages and comprises tasks that need reasoning. XTREME is one of Google’s ai projects that use natural language processing – sentence classification, structured prediction, sentence retrieval, and question answering.
MEENA is a chatbot that handles a wide variety of conversational topics and humanizes computer interaction, improves foreign language practice, and so on. It is an end-to-end trained neural conversational model with a single Evolved Transformer encoder block and 13 Evolved Transformer decoder blocks. These blocks help them to respond sensibly by minimizing the perplexity and the uncertainty in prediction.
Here is the list of Artificial Intelligence projects through which you can adapt to new job trends or excel in your AI-based career. Only hands-on experience on AI projects can make you a sharp mind to provide valuable insights on challenges faced by today’s world. Now it’s time for you to explore the project ideas through our AI projects and gain skills that companies seek from developers.
1. What are Artificial Intelligence projects?
Artificial Intelligence projects are intelligent projects that make machines capable of executing a task that requires human intelligence. The goal of these intelligent agents includes learning, reasoning, problem-solving, and perception. AI includes many theories, methods, and technologies. It consists of many subfields, such as machine learning, neural network, deep learning, cognitive computing, computer vision, and natural language processing. The additional technologies that support AI are Graphical processing unit, Internet of Things, Advanced algorithms, and API.
2. How do I start an AI project?
Gaining skills in AI projects opens a lot of opportunities. There are plenty of options available for those who want to start an AI project. One efficient way is to enroll in an online course. Choose an area of the topic you are interested in and opt for a course that offers real-world projects.
3. What are the 4 types of AI?
We can classify AI into 4 distinct types.
- Reactive machine
Reactive machines are the type of AI systems that do not use any experience to perform the current task. They do not form any memory and act based on what it sees. Deep Blue, IBM’s chess-playing supercomputers, is an example.
- Limited memory
Limited memory uses experience to act in present situations. An example of limited memory is autonomous vehicles.
- Theory of mind
Theory of mind is a type of AI system that makes machines capable of decision making. None of them is extremely capable of decision making as that of humans. But, it is showing significant progress.
Self-aware is a type of AI system that is aware of themselves. These types of systems should be conscious about themselves, must be aware of their internal state, and should be able to predict others’ feelings.
4. How does AI work?
Data is the new oil. AI works by combining a large amount of data, and intelligent algorithms to help the system learn automatically from data models. AI adds intelligence to your existing application through progressive learning algorithms. This algorithm can be a classifier or a predictor.
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