{ "source_file": "0930", "source_platform": "Medium", "author": "Shawhin Talebi", "title_listed": "5 AI Projects You Can Build This Weekend (with Python)", "keywords": "AI项目,Python", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/5-ai-projects-you-can-build-this-weekend-with-python-c57724e9c461/", "publish_date": "2024-10", "corpus_stage": "预训练/SFT", "notes": "", "id": "509dc7fd9ec8", "collected_at": "2026-10-01T22:27:49", "title": "5 AI Projects You Can Build This Weekend (with Python) | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# 5 AI Projects You Can Build This Weekend (with Python)\nFrom beginner-friendly to advanced\n[Shaw Talebi](https://towardsdatascience.com/author/shawhin/)\nOctober 9, 20247 min read\n![Image from Canva.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/10/1YMwyctXXY6dsjyW_CVv0lQ.png)\nImage from Canva.\nThe best way to develop your AI skills is by **building projects**. However, figuring out _what to build_ can be difficult if you're just getting started. Here, I share 5 AI projects you can build fast at three levels of sophistication. I'll break down the steps and Python libraries needed to implement each idea.\n* * *\nPlease accept cookies to access this content\nThe **number one mistake** beginners make when thinking of project ideas is starting with the question, _\"How can I use this new tech?_ \" While this can be a fine way to learn a new tool, there is a better way.\nGood project ideas start with the question, _\"What problem can I solve?\"_ This not only makes for a nice story when sharing with potential employers but solving problems is how you **translate technical skills into value**.\nThe following projects all take this problem-first approach. You can take these ideas and implement them directly or (even better) use them as inspiration for solving a problem that you are personally facing.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## **1) Resume Optimization (Beginner)**\nAn effective yet time-consuming part of applying for jobs is adapting your resume to different job descriptions. While automating this task would have been an advanced project a few years ago, with today's large language models, it is as simple as an [API call](https://towardsdatascience.com/cracking-open-the-openai-python-api-230e4cae7971).\nHere's a step-by-step breakdown of how to implement such an automation.\n 1. Create a markdown version of your resume _(Note: ChatGPT can do this for you)._\n 2. Experiment with different prompt templates that take your markdown resume and a job description and output a new resume in markdown.\n 3. Use OpenAI's Python API to prompt GPT-4o-mini to rewrite your resume dynamically.\n 4. Convert the [markdown](https://pypi.org/project/Markdown/) file to HTML and then to PDF with the _markdown_ and [_pdfkit_](https://pypi.org/project/pdfkit/) libraries, respectively.\n\n\n_**Libraries**_ _:_[_openai_](https://pypi.org/project/openai/) _,_[_markdown_](https://pypi.org/project/Markdown/) _,_[_pdfkit_](https://pypi.org/project/pdfkit/)\nWhile we could readily use ChatGPT for this, the upside of implementing this with Python is that we can easily scale up the process. Here's some starter code for Step 3.\npython\n\n```\nimport openai\nopenai.api_key = \"your_sk\"\n\n# prompt (assuming md_resume and job_desciption have been defined)\nprompt = f\"\"\"\nI have a resume formatted in Markdown and a job description. \nPlease adapt my resume to better align with the job requirements while \nmaintaining a professional tone. Tailor my skills, experiences, and \nachievements to highlight the most relevant points for the position. \nEnsure that my resume still reflects my unique qualifications and strengths \nbut emphasizes the skills and experiences that match the job description.\n\n### Here is my resume in Markdown:\n{md_resume}\n\n### Here is the job description:\n{job_desciption}\n\nPlease modify the resume to:\n- Use keywords and phrases from the job description.\n- Adjust the bullet points under each role to emphasize relevant skills and achievements.\n- Make sure my experiences are presented in a way that matches the required qualifications.\n- Maintain clarity, conciseness, and professionalism throughout.\n\nReturn the updated resume in Markdown format.\n\n\"\"\"\n\n# make api call\nresponse = openai.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": prompt}\n ], \n temperature = 0.25\n)\n\n# extract response\nresume = response.choices[0].message.content\n```\n\nNote: ChatGPT is super helpful for writing short code snippets (and prompts) like this. If you get stuck, try it for Step 4.\n## **2) YouTube Lecture Summarizer (Beginner)**\nAlthough I love adding technical talks to my YouTube \"watch later\" playlist, it might be a while before I watch them (if I ever get around to it 😅 ). A project that can help with this is a tool that watches the videos for me and generates concise summaries with key points.\nHere's one way to do that:\n 1. Extract YouTube video ID from video link using regex\n 2. Use video ID to extract transcript using [_youtube-transcript-api_](https://pypi.org/project/youtube-transcript-api/)\n 3. Experiment with different ChatGPT prompts that effectively summarize the transcript\n 4. Use OpenAI's Python API to automate the process\n\n\n_**Libraries**_ _:_[_openai_](https://pypi.org/project/openai/) _,_[_youtube-transcript-api_](https://pypi.org/project/youtube-transcript-api/)\nFrom a technical perspective, this is very similar to the first project. A key difference, however, is that we will need to automatically extract video transcripts, which we can feed into the LLM.\nHere's some starter code for that.\npython\n\n```\nimport re\nfrom youtube_transcript_api import YouTubeTranscriptApi\n\nyoutube_url = \"video link here\"\n\n# extract video ID with regex\nvideo_id_regex = r'(?:v=|/)([0-9A-Za-z_-]{11}).*'\nmatch = re.search(video_id_regex, youtube_url)\n\nif match:\n return match.group(1)\nelse:\n return None\n\n# extract transcript\ntext_list = [transcript[i]['text'] for i in range(len(transcript))]\ntranscript_text = 'n'.join(text_list)\n```\n\n## **3) Automatically Organizing PDFs (Intermediate)**\nMy watch later playlist is not the only place I hoard technical information. Another cache is my desktop, which is riddled with (118) research papers. Since manually reviewing these papers would be (very) time-consuming, let's see how AI can help.\nOne could build a tool that analyzes the contents of each PDF on my desktop and organize them into folders based on topics. [Text embeddings](https://towardsdatascience.com/text-embeddings-classification-and-semantic-search-8291746220be) can translate each paper into a dense vector representation, from which similar articles could be clustered using a traditional machine learning algorithm like K-Means.\nHere's a more detailed breakdown:\n 1. Read the abstract of each research article using [_PyMuPDF_](https://pypi.org/project/PyMuPDF/)\n 2. Use the [_sentence-transformers_](https://pypi.org/project/sentence-transformers/) library to translate abstracts into text embeddings and store them in a Pandas DataFrame\n 3. Use your favorite clustering algorithm from sklearn to group the embeddings based on similarity\n 4. Create folders for each cluster and move the files into the appropriate folder.\n\n\n_**Libraries**_ _:_[_PyMuPDF_](https://pypi.org/project/PyMuPDF/), _[sentence_transformers](https://pypi.org/project/sentence-transformers/), [pandas](https://pypi.org/project/pandas/), [sklearn](https://pypi.org/project/scikit-learn/)_\nThe key step for this project is generating the [text embeddings](https://towardsdatascience.com/text-embeddings-classification-and-semantic-search-8291746220be). Here's a code snippet for doing that with sentence_transformers.\nmakefile\n\n```\nfrom sentence_transformers import SentenceTransformer\n\n# load embedding model\nmodel = SentenceTransformer(\"all-MiniLM-L6-v2\")\n\n# store abstracts in a list\nabstract_list = [\"abstract 1\", \"abstract 2\"]\n\n# calculate embeddings\nembeddings = model.encode(abstract_list)\n```\n\n## **4) Multimodal Search (Intermediate)**\nA couple of months ago, I helped a company create a basic [RAG](https://towardsdatascience.com/how-to-improve-llms-with-rag-abdc132f76ac) system for a set of technical reports. One of the challenges with searching such reports is that key information is often presented in **plots and figures rather than text**.\nOne way to incorporate this visual information into the search process is to use a **multimodal embedding model** to **represent text and images in a shared space**.\nHere's a basic breakdown:\n 1. Given a PDF, chunk it into sections and extract the images using [_PyMuPDF_](https://pypi.org/project/PyMuPDF/)\n 2. Use a multimodal embedding model (e.g. [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)) to represent the chunks and images as dense vectors and store them in a dataframe\n 3. Repeat for all PDFs in the knowledge base\n 4. Given a user query, pass it through the same embedding model used for the knowledge base\n 5. Compute the cosine similarity score between the query embedding and every item in the knowledge base\n 6. Return top k results\n\n\n_**Libraries**_ _:_[_PyMuPDF_](https://pypi.org/project/PyMuPDF/) _,_[_transformers_](https://pypi.org/project/transformers/) _,_[_pandas_](https://pypi.org/project/pandas/) _,_[_sklearn_](https://pypi.org/project/scikit-learn/)\nThe most important part of this project is how the PDFs are chunked. The simplest way would be to use a fixed character count with some overlap between chunks. It is also helpful to capture metadata such as filename and page number for each chunk.\nHere's some basic boilerplate code to do that (courtesy of ChatGPT). If you get stuck, try asking it to extract the images.\npython\n\n```\nimport fitz # PyMuPDF\n\ndef extract_text_chunks(pdf_path, chunk_size, overlap_size):\n # Open the PDF file\n pdf_document = fitz.open(pdf_path)\n chunks = []\n\n # Iterate through each page in the PDF\n for page_num in range(len(pdf_document)):\n page = pdf_document[page_num]\n page_text = page.get_text()\n\n # Split the text from the current page into chunks with overlap\n start = 0\n while start < len(page_text):\n end = start + chunk_size\n chunk = page_text[start:end]\n\n # Store the page number with the chunk\n chunks.append((page_num + 1, chunk))\n # Move to the next chunk with the overlap\n start += chunk_size - overlap_size\n\n return chunks\n\n# Parameters for extraction\npdf_path = \"your_file.pdf\"\nchunk_size = 1000 # Size of each text chunk in characters\noverlap_size = 200 # Overlap size in characters\n\ntext_chunks = extract_text_chunks_with_page_numbers(pdf_path, chunk_size, overlap_size)\n\n# Display the chunks with page numbers\nfor i, (page_number, chunk) in enumerate(text_chunks):\n print(f\"Chunk {i + 1} (Page {page_number}):n{chunk}n{'-' * 50}\")\n```\n\n## **5) Knowledge Base QA (Advanced)**\nOver the past year, I've helped almost 100 businesses and individuals build AI projects. By far, the **most common project people ask about** is a document question-answering system. Building on the previous project, we can implement this in a straightforward way.\nIf we've already chunked and stored our documents in a DataFrame, we can convert the multimodal search tool into a multimodal RAG system.\nHere are the steps:\n 1. Perform a search over the knowledge base (like the one created in Project 4)\n 2. Combine user query with top k search results and pass them to a multimodal model.\n 3. Create a simple Gradio user interface for the QA system.\n\n\n_**Libraries**_ : [_PyMuPDF_](https://pypi.org/project/PyMuPDF/) _,_[_transformers_](https://pypi.org/project/transformers/) _,_[_pandas_](https://pypi.org/project/pandas/) _,_[_sklearn_](https://pypi.org/project/scikit-learn/), [_together_](https://pypi.org/project/together/) _/_[_openai_](https://pypi.org/project/openai/) _,_[_Gradio_](https://pypi.org/project/gradio/)\n_Note: Llama 3.2 Vision is free until 2025 via Together AI's API_\nThis project essentially combines projects 2 and 4. However, it includes the essential component of a user interface. For that, we can use a dashboarding tool like **Gradio** , which **allows us to create a chat UI with a few lines of code**.\nHere's an example snippet adapted from Gradio's [doc](https://www.gradio.app/guides/creating-a-chatbot-fast#add-multimodal-capability-to-your-chatbot) for doing this.\npython\n\n```\nimport gradio as gr\nimport time\n\ndef generate_response(message, history):\n \"\"\"\n Your code for generating a response\n \"\"\"\n return response\n\ndemo = gr.ChatInterface(\n fn=generate_response, \n examples=[{\"text\": \"Hello\", \"files\": []}], \n title=\"Echo Bot\", \n multimodal=True)\n\ndemo.launch()\n```\n\n## **What's next?**\nThanks to tools like ChatGPT and [Cursor](https://www.cursor.com/), **it's never been easier to build AI projects** _**fast**_. Things that used to block me for hours (if not days) a few years ago can now be resolved in minutes with advanced coding assistants.\nMy parting advice is to use these tools to **learn faster** and **be bold** in your project choices. For projects, find problems and time-box the implementation into a weekend.\nDrop your questions in the comments :)\n* * *\nI'm hosting a (free) live workshop on **how to build Project 1 -** Resume Optimizer. 👉 [**Learn more**](https://maven.com/p/ba0e76/how-to-build-a-resume-optimizer-with-ai?utm_medium=ll_share_link&utm_source=instructor)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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