5 AI Projects You Can Easily Implement with Python

Building projects is the best way to enhance your AI skills. However, for beginners, “What should I build?” is a common dilemma. This article will share 5 AI projects that can be completed quickly, ranging from basic to advanced, and will gradually explain the steps and Python libraries needed to implement these projects.

Image from Canva

The most common mistake beginners make when thinking about project ideas is starting from the question, “How do I use this new technology?” While this approach can help learn new tools, there is a better way.

A good project idea should start from the question, “What problem can I solve?” This not only makes your project story more attractive when showcasing it to potential employers/investors, but it is also key to translating technical skills into real value.

The following projects all adopt a “problem-oriented” approach. You can implement these ideas directly, or (even better) use them as inspiration to solve problems you personally face.

Beginner Projects

1) Resume Optimization Tool (Beginner)

In the job search process, tailoring your resume to different job descriptions is effective but often time-consuming. A few years ago, automating this task might have been considered an advanced project, but today, thanks to large language models, it has become as simple as calling an API.

Here are the specific steps to implement this automation process:

1. Create a Markdown version of the resume (You can use ChatGPT to generate this automatically).
2. Design different prompt templates to combine your Markdown formatted resume with the job description to generate a new Markdown formatted resume.
3. Use OpenAI’s Python API to call GPT-4o-mini to dynamically rewrite the resume content.
4. Use markdown and pdfkit libraries to convert the Markdown file to HTML and PDF formats, respectively.

Required Libraries: openai, markdown, pdfkit

While you can accomplish this task directly using ChatGPT, the advantage of implementing it in Python is that you can easily scale this process. For instance, you can process multiple job descriptions and resume templates in bulk.

Here is a code example for Step 3:

import openai
openai.api_key = "your_sk"

# prompt (assuming md_resume and job_desciption have been defined)
prompt = f"""
I have a resume formatted in Markdown and a job description. \
Please adapt my resume to better align with the job requirements while \
maintaining a professional tone. Tailor my skills, experiences, and \
achievements to highlight the most relevant points for the position. \
Ensure that my resume still reflects my unique qualifications and strengths \
but emphasizes the skills and experiences that match the job description.

### Here is my resume in Markdown:
{md_resume}

### Here is the job description:
{job_desciption}

Please modify the resume to:
- Use keywords and phrases from the job description.
- Adjust the bullet points under each role to emphasize relevant skills and achievements.
- Make sure my experiences are presented in a way that matches the required qualifications.
- Maintain clarity, conciseness, and professionalism throughout.

Return the updated resume in Markdown format.

"""
    
# make api call
response = openai.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ], 
    temperature = 0.25
)
    
# extract response
resume = response.choices[0].message.content

By doing this, you can significantly improve the efficiency of adjusting your resume while ensuring that the content closely matches the job requirements.

Note: ChatGPT is very useful for writing short code snippets (as well as prompts). If you encounter difficulties in Step 4, you can try asking ChatGPT for help.

2) YouTube Talk Summary Generator (Beginner)

While I love adding technical talk videos to my YouTube