5 AI Projects to Build This Weekend Using Python: From Beginner to Advanced

Getting hands-on with real-world AI projects is the best way to level up your skills. But knowing where to start can be challenging, especially if you’re new to AI. Here, we break down five exciting AI projects you can implement over the weekend with Python—categorized from beginner to advanced. Each project uses a problem-first approach to create tools with real-world applications, offering a meaningful way to build your skills.

1. Job Application Resume Optimizer (Beginner)

Updating your resume for different job descriptions can be time-consuming. This project aims to automate the process by using AI to customize your resume based on job requirements, helping you better match recruiters' expectations.

Steps to Implement:

1. Convert Your Resume to Markdown: Begin by creating a simple markdown version of your resume.

2. Generate a Prompt: Create a prompt that will input your markdown resume and the job description and output an updated resume.

3. Integrate OpenAI API: Use the OpenAI API to adjust your resume dynamically based on the job description.

4. Convert to PDF: Use markdown and pdfkit libraries to transform the updated markdown resume into a PDF.

Libraries: openai, markdown, pdfkit

Code Example:

import openai
import pdfkit

# Replace with your OpenAI API Key
openai.api_key = "your_openai_api_key"

def generate_resume(md_resume, job_description):
    prompt = f"""
    Adapt my resume in Markdown format to better match the job description below. \
    Tailor my skills and experiences to align with the role, emphasizing relevant \
    qualifications while maintaining a professional tone.

    Resume in Markdown:
    {md_resume}

    Job Description:
    {job_description}

    Please return the updated resume in Markdown format.
    """

    response = openai.Completion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )

    return response.choices[0].text

# Sample input resume and job description
md_resume = "Your markdown resume content here."
job_description = "Job description content here."

# Generate updated resume
updated_resume_md = generate_resume(md_resume, job_description)

# Convert updated markdown to PDF
pdfkit.from_string(updated_resume_md, "optimized_resume.pdf")

This project can be expanded to allow batch processing for multiple job descriptions, making it highly scalable.

2. YouTube Video Summarizer (Beginner)

Many of us save videos to watch later, but rarely find the time to get back to them. A YouTube summarizer can automatically generate summaries of educational or technical videos, giving you the key points without the full watch time.

Steps to Implement:

1. Extract Video ID: Use regex to extract the video ID from a YouTube link.

2. Get Transcript: Use youtube-transcript-api to retrieve the transcript of the video.

3. Summarize Using GPT-3: Pass the transcript into OpenAI’s API to generate a concise summary.

Libraries: openai, youtube-transcript-api, re

Code Example:

import re
import openai
from youtube_transcript_api import YouTubeTranscriptApi

# Replace with your OpenAI API Key
openai.api_key = "your_openai_api_key"

def extract_video_id(youtube_url):
    match = re.search(r'(?:v=|\/)([0-9A-Za-z_-]{11}).*', youtube_url)
    return match.group(1) if match else None

def get_video_transcript(video_id):
    transcript = YouTubeTranscriptApi.get_transcript(video_id)
    transcript_text = ' '.join([entry['text'] for entry in transcript])
    return transcript_text

def summarize_transcript(transcript):
    response = openai.Completion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": f"Summarize the following transcript:\n{transcript}"}]
    )
    return response.choices[0].text

# Example usage
youtube_url = "https://www.youtube.com/watch?v=example"
video_id = extract_video_id(youtube_url)
transcript = get_video_transcript(video_id)
summary = summarize_transcript(transcript)

print("Summary:", summary)

With this tool, you can instantly create summaries for a collection of videos, saving valuable time.

3. Automatic PDF Organizer by Topic (Intermediate)

If you have a collection of research papers or other PDFs, organizing them by topic can be incredibly useful. In this project, we’ll use AI to read each paper, identify its subject, and cluster similar documents together.

Steps to Implement:

1. Read PDF Content: Extract text from the PDF’s abstract using PyMuPDF.

2. Generate Embeddings: Use sentence-transformers to convert abstracts into embeddings.

3. Cluster with K-Means: Use sklearn to group documents based on their similarity.

4. Organize Files: Move documents into folders based on their clusters.

Libraries: PyMuPDF, sentence_transformers, pandas, sklearn

Code Example:

import fitz  # PyMuPDF
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
import os
import shutil

# Load pre-trained model for embeddings
model = SentenceTransformer('all-MiniLM-L6-v2')

def extract_abstract(pdf_path):
    pdf_document = fitz.open(pdf_path)
    abstract = pdf_document[0].get_text("text")[:500]  # First 500 characters
    pdf_document.close()
    return abstract

# Process PDFs and generate embeddings
pdf_paths = ["path/to/pdf1.pdf", "path/to/pdf2.pdf"]
abstracts = [extract_abstract(pdf) for pdf in pdf_paths]
embeddings = model.encode(abstracts)

# Clustering
kmeans = KMeans(n_clusters=3)
labels = kmeans.fit_predict(embeddings)

# Organize PDFs into folders based on clusters
for i, pdf_path in enumerate(pdf_paths):
    folder_name = f"Cluster_{labels[i]}"
    os.makedirs(folder_name, exist_ok=True)
    shutil.move(pdf_path, os.path.join(folder_name, os.path.basename(pdf_path)))

This organizer can be customized to analyze entire libraries of documents, making it an efficient tool for anyone managing large digital archives.

4. Multimodal Document Search Tool (Intermediate)

Key information may be embedded in both text and images in technical documents. This project uses a multimodal model to enable searching for information within text and visual data.

Steps to Implement:

1. Extract Text and Images: Use PyMuPDF to extract text and images from each PDF section.

2. Generate Embeddings: Use a multimodal model to encode text and images.

3. Cosine Similarity for Search: Match user queries with document embeddings based on similarity scores.

Libraries: PyMuPDF, sentence_transformers, sklearn

Code Example:

import fitz  # PyMuPDF
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# Load model for multimodal embeddings
model = SentenceTransformer('clip-ViT-B-32')  # CLIP model for multimodal encoding

def extract_text_and_images(pdf_path):
    pdf_document = fitz.open(pdf_path)
    chunks = []
    for page_num in range(len(pdf_document)):
        page = pdf_document[page_num]
        chunks.append(page.get_text("text")[:500])  # Extracting text
        for img in page.get_images(full=True):
            chunks.append("image_placeholder")  # Placeholder for image embedding
    pdf_document.close()
    return chunks

def search_query(query, documents):
    query_embedding = model.encode(query)
    doc_embeddings = model.encode(documents)
    similarities = cosine_similarity([query_embedding], doc_embeddings)
    return similarities

# Example usage
pdf_path = "path/to/document.pdf"
document_chunks = extract_text_and_images(pdf_path)
similarities = search_query("User's search query here", document_chunks)
print("Top matching sections:", similarities.argsort()[::-1][:3])

This multimodal search tool makes it easier to sift through complex documents by combining text and visual information into a shared search index.

5. Advanced Document QA System (Advanced)

Building on the previous project, this system allows users to ask questions about documents and get concise answers. We use document embeddings to find relevant information and a user interface to make it interactive.

Steps to Implement:

1. Chunk and Embed: Extract and embed each document’s content.

2. Create Search + QA System: Use embeddings for search and integrate with OpenAI’s API for question-answering.

3. Build an Interface with Gradio: Set up a simple Gradio UI for users to input queries and receive answers.

Libraries: PyMuPDF, sentence_transformers, openai, gradio

Code Example:

import gradio as gr
import openai
from sentence_transformers import SentenceTransformer

# Load embedding model
model = SentenceTransformer("all-MiniLM-L6-v2")

# Placeholder for PDF extraction and embedding functions
def generate_response(message, history):
    response = openai.Completion.create(
        model="gpt-3.5-turbo",


 messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].text

# Gradio interface
demo = gr.ChatInterface(
    fn=generate_response,
    examples=[{"text": "Explain this document section"}]
)

demo.launch()

This interactive QA system, using Gradio, brings conversational AI to documents, enabling users to ask questions and receive relevant answers.

These weekend AI projects offer practical applications for different skill levels. From resume optimization to advanced document QA, these projects empower you to build AI solutions that solve everyday problems, sharpen your skills, and create impressive additions to your portfolio.