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7 AI Projects for All Levels

Develop your portfolio and improve your skills in creating innovative solutions for complex problems by working on AI projects.
Updated Mar 2024  · 8 min read

We live in exciting times where artificial intelligence (AI) and chatbots like ChatGPT are dominating conversations. It seems everyone wants their own AI assistant these days. But do you want to build your own AI project from the ground up?

In this post, we'll look at AI projects for beginners, intermediate developers, and experts. You'll find resources to guide you through creating your own AI application, whether you're just starting out or have some experience under your belt.

Beginner AI Projects

These AI projects for beginners can help you gain confidence and develop new skills, ensuring you get to grips with the essentials of artificial intelligence.

1. Give Life: Predict Blood Donations

The Give Life: Predict Blood Donations project aims to predict whether a donor will donate blood within a given time window or not. The dataset used in this project is from a mobile blood donation vehicle in Taiwan that collects blood from various universities as part of a blood donation drive.

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In this AI project, you will process the raw data and input it into the TPOT Python AutoML tool. This tool will search through hundreds of machine learning pipelines to identify the best one for our dataset.

Once we have identified the ideal pipeline, we will create our model using normalized features to achieve a better score.

AutoML tools allow even beginners with limited machine learning knowledge to build AI projects. These tools handle all the tasks and provide the best-performing model for testing and deployment.

2. Predicting Credit Card Approvals

In the Predicting Credit Card Approvals project, you will create an application for automatic credit card approval using hyperparameter optimization and Logistic Regression.

For this AI project, you will need to use various skills, such as handling missing values, processing categorical features, scaling features, dealing with unbalanced data, and applying automatic hyperparameter optimization through GridCV.

This project is designed to challenge you beyond the realm of simple and clean data.

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If you want to become an expert in classification, why not build another project using the Loan Data from LendingClub.com? You can use the dataset to create an automatic loan approval predictor.

If you're seeking projects suitable for beginners, then DataCamp has a library of Data Science Projects that you may want to explore. By applying your coding skills to a variety of datasets, you'll be able to tackle practical challenges in your browser and gain real-world experience.

Intermediate AI Projects

We’ve picked these AI projects for intermediate practitioners as they build on the foundational skills you should already have. They’re challenging enough to be fun while still developing your skills.

3. Object Detection

In the Object Detection project, the first step is to break down the image into tiles. Then, you will use a pre-trained VGG-16 CNN to predict the probability of a cat being present in the image. Finally, you will create a heatmap of probabilities to indicate the location of the cat within the image.

Image from project

In order to provide a more accurate location for a bounding box, you must use a regression model to predict its coordinates.

When dealing with objects that have a more complicated shape, it is beneficial to use CNNs for this task. To achieve this, you will utilize a stochastic gradient descent optimizer and mean squared error (MSE) as the metrics, as our aim is to perform regression. Finally, you will evaluate the model's results.

4. BERT for Text Classification

In the Text Classification project, you will use BERT (Bidirectional Encoder Representations from Transformers) to predict whether a given NEWS article falls under the category of "World," "Sports," "Business," or "Sci/Tech."

This intermediate AI project will teach you how to handle the dataset, build and train the model, and ultimately assess the model’s performance. Additionally, you will learn about attention mechanisms, transformers, and understand BERT architecture.

Image from project

Including this project in your machine learning portfolio can increase your chances of being hired. In addition to learning how to train large AI models, you will also gain technical knowledge on how these large and complex models operate.

Discover our article on Effective AI Project Management Techniques. This blog post brings together the top management strategies for handling AI projects on a large scale.

Expert AI Projects

These AI Projects for experts will really challenge you. To complete them, you’ll need to use a broad array of skills and knowledge and try new concepts that will help you develop.

5. Stock Market Analysis And Forecasting Using LSTM

Discover and explore stock market data, focusing on technology stocks such as Apple, Amazon, Google, and Microsoft in the Stock Market Analysis and Forecasting using the LSTM project.

Learn how to retrieve stock information using the yfinance library and visualize it using Seaborn and Matplotlib. Analyze a stock's risk based on its previous performance history. Finally, build an AI model using the Long Short Term Memory (LSTM) method to predict future stock prices.

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Having knowledge of handling time series datasets and the ability to forecast stock prices is crucial for anyone interested in the financial sector or research-oriented fields.

Similarly, being able to comprehend trends and provide solutions is a highly sought-after skill in the finance industry.

6. Super Mario AI

In the Super Mario AI project, you will train an AI agent to play the first level of Super Mario World using deep Q-learning and raw pixel input. It combines techniques like experience replay, a spatial transformer network, and an ε-greedy policy.

The model architecture has separate branches for processing action history, screenshot history, and the current state, which are merged before the output layer predicts action-reward values. Through observing gameplay screens, it learns to play the level without hand-engineered features.

GIF from project

This project is highly advanced and requires extensive proficiency in both reinforcement learning and computer vision. While it may be difficult to replicate, the effort is well worth it.

If you're seeking more challenging projects, consider exploring "25 Machine Learning Projects for All Levels." These projects can assist you in covering all of the major fields within machine learning.

AI Projects for Fun

We finish our list of AI projects with a quick, fun one. Even if you’re new to the field of artificial intelligence, you’ll find this one enjoyable.

7. Build AI Chatbot in 5 Minutes

In the AI for Fun project, you will build an AI Chatbot using Hugging Face and Gradio. It is quite simple and requires minimal knowledge of Python programming.

In this project, you will learn to build and deploy the web application using transformers to load the conversational model and use gradio Chat inference to create chatbot user inference. It requires 5 minutes to build an app, and you don't need to leave your browser.

Image from AI ChatBot

Image from AI ChatBot

Gradio has recently introduced gr.ChatInterface(predict), which allows users to easily build customized chatbots and integrate them with agents in less than 5 minutes. For a complete guide on how to create your own chat interface, visit the Creating A Chatbot Fast page on gradio.app.

If you enjoyed building AI projects for fun, why not try 5 Projects Build with Generative Models and Open Source Tools? You will learn to create an image editor, a ChatGPT-like chatbot on low resources, a loan approval classifier app, automate PDF Interactions, and a GPT-powered voice assistant.

Conclusion

Once you have completed your basic education and obtained your degree, it is highly recommended that you work on AI projects to build your portfolio. In addition to coding, it is important to document your project and share it with other professionals to receive feedback. A strong portfolio will help you secure your dream job and excel in the field of machine learning and data science.

This blog provides an overview of beginner, intermediate, expert, and fun AI projects. If you are new to the field of AI, you can start your journey by enrolling in AI Fundamentals. This program will provide you with actionable knowledge on popular AI topics such as ChatGPT, large language models, generative AI, and more. Start learning today to be prepared for the AI world of tomorrow.


Photo of Abid Ali Awan
Author
Abid Ali Awan

I am a certified data scientist who enjoys building machine learning applications and writing blogs on data science. I am currently focusing on content creation, editing, and working with large language models.

FAQs

Do I need a strong programming background to start with the beginner AI projects?

No, beginner projects are designed to help you develop skills even with limited programming knowledge, especially since tools like TPOT AutoML are used to simplify the process.

What are AutoML tools, and how do they help in AI projects?

AutoML tools, such as TPOT, automatically search through machine learning pipelines to find the best one for your dataset, making it easier to build AI models without deep expertise.

Can I work on intermediate projects without completing any beginner projects?

Yes, if you have foundational skills in AI and programming, you can directly start with intermediate projects to challenge yourself and learn more advanced concepts.

How can I learn AI from scratch?

If you have no prior experience or AI skills, it's likely too soon for you to embark on an AI project. Fortunately, DataCamp is the best place to kickoff your AI journey! Our AI Fundamentals Track will get you up to speed with important AI concepts, and our How to Learn AI From Scratch guide will take you through exactly the steps to becoming an AI expert.

How can I become an AI engineer?

To become an AI engineer, start by gaining a solid foundation in areas like AI, Machine Learning (ML), data science, and software engineering, as these disciplines are crucial for designing and developing intelligent solutions. Then, focus on practical experience through internships, personal projects (such as the projects from the article), or online courses and bootcamps to build and showcase your skills.

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