How to Build Your First AI Project: A Step-by-Step Guide to Resume Matching Using NLP

Introduction

If you're starting out in AI and looking for your first hands-on project, this one's perfect. We'll build a Resume Matcher— an NLP-based tool that compares your resume to a job description and shows how well they align.

Along the way, you’ll learn key NLP concepts like lemmatization, TF-IDF, and cosine similarity, all explained in a beginner-friendly way — and with a bit of history, trivia, and fun facts sprinkled in to keep it engaging!

You can also run the full working code in your browser using Google Colab (no setup needed): Open Resume Matcher in Google Colab

What You'll Build

You’ll create a Python-based tool that:

• Accepts a resume and a job description (text or PDF)

• Preprocesses both using NLP techniques

• Converts the text into numeric vectors using TF-IDF

• Calculates a match score based on cosine similarity

• Optionally displays missing keywords in your resume

Let’s get started step by step.

Tools & Libraries

Install these Python packages:

pip install scikit-learn nltk PyPDF2

📜 Fun Fact: The NLTK (Natural Language Toolkit) library has been around since 2001 and was originally developed at the University of Pennsylvania. It’s one of the first open-source libraries aimed at teaching NLP!

Step 1: Read the Resume and Job Description

You can either paste the text directly or read from a PDF file.

# Option 1: Paste resume and JD resume_text = """Experienced backend engineer with skills in Java, Spring Boot, AWS...""" job_description = """We are looking for a developer with experience in Java, Docker, AWS...""" # Option 2: Extract from PDF import PyPDF2 def extract_text_from_pdf(file_path): with open(file_path, 'rb') as file: reader = PyPDF2.PdfReader(file) return " ".join(page.extract_text() for page in reader.pages) resume_text = extract_text_from_pdf("resume.pdf")

🧾 Did you know? PDF (Portable Document Format) was created by Adobe back in 1993 and remains one of the most common document formats — which is why parsing it is so important in projects like these.

Step 2: Preprocess the Text

Before comparing the texts, we clean and normalize them using NLP.

Key Techniques:

• Lowercasing: For uniform comparison

• Tokenization: Splits text into words

• Stopword Removal: Removes common words like "and", "is", "the"

• Lemmatization: Converts words to base forms (e.g., "running" → "run")

import nltk import string from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer nltk.download('punkt') nltk.download('stopwords') nltk.download('wordnet') def preprocess(text): text = text.lower() text = text.translate(str.maketrans('', '', string.punctuation)) tokens = nltk.word_tokenize(text) tokens = [t for t in tokens if t not in stopwords.words('english')] lemmatizer = WordNetLemmatizer() lemmatized = [lemmatizer.lemmatize(t) for t in tokens] return " ".join(lemmatized) resume_clean = preprocess(resume_text) jd_clean = preprocess(job_description)

🤖 Fun Insight: Lemmatization is inspired by linguistics. The word "lemmatize" comes from the Greek word lemma, meaning "assumption" or "base form." It helps machines understand that "drives" and "driving" are essentially the same as "drive."

Step 3: Convert Text to Vectors with TF-IDF

TF-IDF (Term Frequency-Inverse Document Frequency) is a technique that transforms text into numerical vectors — giving higher weight to words that are important in one document but rare across others.

from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer() vectors = vectorizer.fit_transform([resume_clean, jd_clean])

📈 Trivia: TF-IDF was first introduced in the 1970s by Gerard Salton. It became one of the most important formulas in early search engines before Google made PageRank famous!

Step 4: Calculate Match Score using Cosine Similarity

Cosine similarity measures how similar two documents are based on the angle between their vector representations. A score close to 1 means strong similarity.

from sklearn.metrics.pairwise import cosine_similarity score = cosine_similarity(vectors[0:1], vectors[1:2])[0][0] match_percent = round(score * 100, 2) print(f"Resume Match Score: {match_percent}%")

📐 Fun Geometry Alert: Cosine similarity is based on the cosine of the angle between vectors — an idea borrowed directly from trigonometry. Who said high school math wouldn’t be useful?

Step 5: Show Missing Keywords

Let’s help the user see which important job keywords are missing from their resume.

resume_words = set(resume_clean.split()) jd_words = set(jd_clean.split()) missing_keywords = jd_words - resume_words print("Consider adding these keywords:", ", ".join(sorted(missing_keywords)))

🎯 Tip: This is exactly what some resume scanners (ATS) do — they filter resumes based on keywords. So now you're not just learning AI, you’re also reverse-engineering real-world systems.

Summary: What You Learned

This hands-on project combines practical coding with real-world application, making it a fantastic entry point into AI and NLP.

What’s Next?

This was just the beginning! You now have a working Python prototype. Here are a few exciting ways you can level it up:

• Turn it into a full-fledged web app using Streamlit, Flask, or React + FastAPI.

• Add a PDF uploader so users can upload their resumes directly.

• Let GPT or another LLM suggest resume improvements in real time.

• Build a resume report card that visually scores and ranks resumes.

• Add a feedback loop — collect user responses and improve the model over time.

Fun Fact:

Google receives over 3 million job applications per year — and guess what? Many companies now use ATS (Applicant Tracking Systems) powered by similar NLP techniques to filter resumes. So what you’ve just built? It’s surprisingly close to real-world hiring tech!

Ready to take this project live? My follow-up blog shows you how I deployed this exact tool as a working web app. Check it out here: How I Built a Live Resume Matching Tool with React and FastAPI