5 AI Projects to Build with Python

AI development has come a long way, and if you’re looking to advance your skills, there’s no better way than building hands-on projects. But often, the hardest part is figuring out what to build. In this article, I’ll walk you through five AI projects that you can quickly build using Python. Each project is designed to address a real problem, with a step-by-step breakdown and the Python libraries you’ll need.

Get Jackson Davis’s stories in your inbox

Join Medium for free to get updates from this writer.

Remember me for faster sign in

These projects don’t just help you practice new tech skills — they’re also great conversation starters for job applications and interviews. Let’s dive in.

1. Automate Resume Customization with AI

One of the most tedious tasks for job seekers is tailoring resumes to match job descriptions. Luckily, with modern large language models (LLMs), you can automate this process with just a few lines of Python.

Steps:

1. Create a markdown version of your resume: Use a tool like ChatGPT to convert your existing resume into markdown format.
2. Experiment with prompts in ChatGPT: Input both the markdown resume and a job description, and ask ChatGPT to output a tailored version of your resume.
3. Move from ChatGPT to Python: Use the OpenAI Python API to automate the process of rewriting resumes dynamically with GPT-4.
4. Convert markdown to PDF: Using markdown and pdfkit Python libraries, you can easily convert your newly customized resume back into a PDF format.

Why build this? This project helps you understand how to integrate AI with daily tasks, like job applications, while giving you valuable experience with APIs, markdown, and file conversion.

2. Summarize YouTube Videos Automatically

If you’re anything like me, you probably have a growing list of “Watch Later” videos on YouTube that never seem to get watched. Why not build a tool that watches those videos for you and generates concise summaries?

Steps:

1. Extract the YouTube video ID: Using regular expressions, pull out the video ID from a YouTube URL.
2. Get the transcript: Use the YouTube Transcript API to fetch the video’s transcript.
3. Summarize the transcript with AI: Experiment with prompts in ChatGPT to summarize the transcript effectively.
4. Automate the process: Use OpenAI’s Python library to automate the entire process, creating a summary for every new video link you input.

Why build this? This project teaches you how to use APIs, work with video data, and leverage AI for text summarization, all while solving a personal problem.

3. Organize Research Papers Using AI Clustering

For those of us who hoard research papers (guilty!), sorting through hundreds of PDFs can be overwhelming. Let’s use AI to group similar papers together and declutter your desktop.

Steps:

1. Read the abstracts: Use the PyMuPDF Python library to extract abstracts from your PDF files.
2. Generate text embeddings: Apply the SentenceTransformers library to convert the abstracts into vector representations.
3. Cluster the papers: Use sklearn’s K-means clustering algorithm to group similar papers.
4. Sort the papers into folders: Create directories for each cluster and move the relevant papers into the appropriate folders.

Why build this? This project gives you experience with text embeddings and machine learning algorithms while helping you make sense of large volumes of unstructured data.

4. Build a Multimodal Search Tool for PDFs

Searching technical documents or research reports can be tricky, especially when key information is stored in images and charts. A multimodal search tool allows you to search text and images simultaneously.

Steps:

1. Chunk the PDF: Break down the PDF into sections and extract images using PyMuPDF.
2. Use a multimodal model: Download a multimodal embedding model from the HuggingFace Hub and use it to generate vector representations for both the text and images.
3. Compute similarity: Using sklearn, calculate the cosine similarity between the user query and the document embeddings.
4. Return search results: Present the most relevant text or images based on the search query.

Why build this? This project challenges you to combine different types of data — text and images — using AI models, providing valuable skills for real-world applications in data search and retrieval.

5. Create a Knowledge-Based Question Answering System

Building on the previous project, you can create a knowledge-based question answering (QA) system. This allows users to input queries and receive precise answers based on the content of documents.

Steps:

1. Search the knowledge base: Use the search method from the multimodal project to retrieve the top results for a user query.
2. Combine search results with the user query: Pass both the query and the retrieved results to a multimodal model (such as GPT-4 or LLaMA 3.2 Vision) for detailed answers.
3. Build a user interface: Create a simple user interface with Gradio, a Python library that lets you create web apps quickly.

Why build this? This project combines search algorithms with an interactive interface, giving you hands-on experience in both AI development and UI design.

Key Takeaways

1. Start with a problem, not a technology: When building projects, focus on solving real problems. This makes your work more valuable and relevant to employers.
2. Leverage AI tools: Tools like ChatGPT and OpenAI’s API can help you automate tedious tasks, making you a more productive programmer. Don’t be afraid to use coding assistants to accelerate your learning and project building.

These projects not only help you develop AI skills but also provide practical, problem-solving experience that you can showcase to potential employers. Got any questions or need help with these project ideas? Drop a comment below — I’d love to hear from you!