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Top 16 Artificial Intelligence Project Ideas & Topics for Beginners [2024]

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26th Feb, 2023
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Top 16 Artificial Intelligence Project Ideas & Topics for Beginners [2024]

Artificial intelligence controls computers to resemble the decision-making and problem-solving competencies of a human brain. It works on tasks usually linked with learning or thinking, including reasoning and self-correction. Moreover, artificial intelligence blends robust datasets and computer science to derive solutions to the problem. You can design scalable AI-based solutions and acquire self-learning via practical applications.

The choice of artificial intelligence projects depends on various factors like your interest, budget, time, and trending topics.

Let’s look at some exciting AI project ideas and topics for beginners to improve their skills and enhance their portfolios.

Top AI Project Ideas & Topics

1. Fake News Finder

Fake news means false or ambiguous information spread to misguide people. Occasionally, fake news is presented so professionally that people completely trust it. It is imperative to differentiate between original news and fake news. If not detected early, it can create many unimaginable issues.

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Utilize the Real and Fake News dataset available on Kaggle to develop a fake news detector project. The classification of fake and original news occurs via a pre-trained ML model known as BERT. Essentially, it is an open-source NLP model being loaded into Python.

2. Teachable Machine

Working on a Teachable Machine is one of the most interesting artificial intelligence project ideas for beginner-level AI enthusiasts. A Teachable Machine refers to a web-based tool developed to offer people easy access to machine learning functionalities. Its website allows you to upload images of various classes. Subsequently, you can train a client-side ML model on those images. This project enables you to learn many potent machine-learning functionalities.

3. Autocorrect Tool

When you start working on such AI based projects, you can gradually streamline your everyday tasks. Autocorrect application of AI is used in daily life, which assists in correcting spelling and grammatical errors.

 You can build this project in Python using its TextBlob library. Its function ‘correct()’ will be helpful for this project.

4. Fake Product Review Identification

It is one of those AI projects for beginners that can deter business owners who usually upload fake product reviews on their websites. Its implementation will ensure that customers will not be diverted to false product reviews when they perform their product research. You can use Kaggle to build this project. Kaggle contains a Deceptive Opinion Spam Corpus dataset with 1600 reviews (800 positive and 800 negative reviews).

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5. Plagiarism Analyzer

Plagiarism Analyzer is one of the most prevalent artificial intelligence project ideas. The reason is it can detect plagiarism which is imperative to ensure original content. It can be challenging to determine the originality of the content without using a tool. This project helps you to build a plagiarism analyzer application to ensure originality and authenticity across a piece of content.

6. Bird Species Predictor

Topic experts can manually classify birds, but the process can be challenging and monotonous since it needs a massive data collection. The Bird Species Predictor project uses AI-based categorization, which uses a random forest to predict bird species.

7. Stock Price Predictor

It is one of the most valuable artificial intelligence projects for finance professionals and students aspiring to embark on a career in finance. This project provides access to a broad range of datasets. These datasets let you learn how to use ML algorithms to inspect a considerable amount of data. The availability of a vast amount of data simplifies finding models and patterns. Ultimately, it becomes easy to predict the future stock market precisely.

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8. Customer Advice System

It is one of the most prominent AI project ideas for those business owners willing to understand the customers’ product preferences. It uses a customer advice system to gain instant feedback on customers’ opinions of products. You need to build a real-time message tool within your e-commerce app. It helps you to communicate with customers and discerns their opinions regarding the products.

9. Lane Line Recognition

AI based projects are valuable for vehicles too. This project helps you develop a system connecting line-following robots and self-driven vehicles. So they can have real-time analysis of lane lines on a road. If self-driving cars are not effectively trained, it can lead to roadside accidents. This project solves this problem by using Python’s Computer Vision. It contributes to effectively detecting self-driven vehicles and reduces the risks of roadside accidents. Python’s OpenCV library helps you to accomplish this project.

10. Handwritten Digit Recognition

This project aims to develop a system that can identify handwritten digits using artificial neural networks. Usually, characters and digits written by humans represent different sizes, shapes, styles, and curves. The computers must be able to identify manual writing. The mentioned project uses artificial neural networks to develop a handwritten digit identification system to decode the digits that humans write accurately. CNN (Convolution Neural Network) is used for identifying digits on paper.

11. Pneumonia Detection

It is one of the most useful AI project ideas to detect pneumonia and ensure good health for people. Capturing patients’ X-ray images help you to detect diseases like a tumor, cancer, pneumonia, etc. But the images feature low visibility, and interpretation can be complex. This project aims to develop an AI system using CNN (Convolution Neural Networks) to identify pneumonia from a patient’s X-ray pictures effectively. It trains software solutions to detect and interpret this disease’s results accurately. The software processes the relevant information and tests it in the built-in database.

12. Recommendation System for Customers

It is one of the most versatile and prevalent AI projects for beginners in customer management. It builds a recommendation system that helps customers infer more details on products, music, video, and more. It uses concepts of machine learning, data mining, and ANN. The system drives more customers to the website and ultimately boosts the sales of a business. 

13. Recognizing the genre of a song

This project imparts AI knowledge to beginners in an easy and fun-filled way. It is one of the famous mini-AI projects that gradually strengthen your AI skills. It helps you to recognize a song’s genre.

It uses an artificial neural network to identify the song and its genre. Subsequently, it showcases the appropriate playlist. You need to use Python’s Librosa library to derive all the necessary details of the song.

14. Predicting users’ forthcoming location

Travelers usually find it difficult to explore, especially when they travel to unaccompanied places. This AI project predicts the user’s most likely next location. It can be a restaurant or holiday venue. The projects make informed decisions using the LempelZiv (LZ) algorithm, Neural Networks (NNs), Markov Model (MM), Association rules, and  Bayesian Networks. 

15. Translator app

The translator app is an AI project that uses NLP fundamentals. It helps you to develop a translator app that helps translate a sentence from an unfamiliar language to your native one. It can be challenging and laborious to train an AI model from the beginning. However, you can use this project’s pre-trained models called ‘transformers’ that help you to translate any sentence easily. Python’s GluonNLP library can greatly assist in creating this app.

16. Housing Price Predictor

This project idea uses fundamental AI features to estimate home price variations. It also uses ML models and algorithms. To develop your dataset, you must download a public dataset from web scraping or Kaggle.

The next step is to clean the dataset by determining different null values, anomalies, duplicate entries, etc. Subsequently, you need to calculate different related histograms. As the project progresses, you will be acquainted with test web scraping methods and huge datasets to hold proficiency in the same.  

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You can explore job opportunities as a Data Scientist, Senior Data Analyst, Mathematician/ Statistician, Big Data Engineer/ Data Engineer, Software Developer, etc., after completing this course.


Kick-start your career by working on such AI projects for beginners and gradually working on more advanced ones to enhance your skills and portfolio. These projects can fuel your growth, enhancing your skills and experience level simultaneously. So, make sure to work on any of the AI projects listed here and start soon!



Blog Author
Meet Sriram, an SEO executive and blog content marketing whiz. He has a knack for crafting compelling content that not only engages readers but also boosts website traffic and conversions. When he's not busy optimizing websites or brainstorming blog ideas, you can find him lost in fictional books that transport him to magical worlds full of dragons, wizards, and aliens.
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Frequently Asked Questions (FAQs)

1Q1. What are the four types of AI?

A. (i) Reactive AI: It is the fundamental type of AI programmed to offer a predictable output as per the input. (ii) Limited Memory AI: It learns from past data and develops empirical knowledge based on observation of data or actions. (iii) Theory of Mind AI: This AI type can comprehend and remember emotions. (iv) Self-aware AI: Machines equipped with this AI type are automatically aware of their mental states and emotions.

2Q2. How to manage AI projects?

A. You need to consider six steps when managing AI projects. They are listed below. (i) Problem identification (ii) Testing the problem solution (iii) Data management (iv) Choosing the suitable algorithm (v) Training the algorithm (vi) Product deployment on the suitable platform.

3Q3. What are the reasons behind AI projects' failure?

A. AI projects fail due to these reasons. (i) AI follows a data-centric approach that implies insufficient time or funds to collect data. (ii) Improper planning for continued AI, data iteration, model, and lifecycle. (iii) Misalignment of real-world data and communication against training models and data.

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