AI Resume Analyzer
Leveraging NLP & Deep Learning for
Intelligent Resume Screening
By [Your Name] | [Date]
Introduction
• - AI Resume Analyzer automates resume
screening using AI.
• - Helps companies find the best candidates
efficiently.
• - Assists job seekers in improving their
resumes.
• - Manual resume screening is time-consuming;
AI ensures accuracy.
Objectives
• - Automate resume screening using AI & NLP.
• - Provide resume ranking based on job
requirements.
• - Generate feedback & improvement
suggestions.
• - Improve hiring efficiency and job-seeker
success.
Features
• For HR / Recruiters:
• ✔ Upload multiple resumes at once
• ✔ AI-based ranking of resumes
• ✔ Job-specific resume filtering
• ✔ Automated shortlisting
• For Job Seekers:
• ✔ Instant resume feedback
• ✔ Resume Score & Suggestions
Technologies Used
• - Frontend: HTML, CSS, JavaScript
• - Backend: Python (Flask/Django)
• - Database: PostgreSQL / MongoDB
• - AI Models: NLP & Deep Learning
• - Libraries: Spacy, NLTK, TensorFlow, PyTorch,
OpenAI GPT / BERT
Methodology (Step-by-Step)
• 1. Data Collection: Gather resume datasets
• 2. Preprocessing: Tokenization, Stop-word
removal
• 3. Feature Extraction: TF-IDF, Word
Embeddings
• 4. Model Training: Deep Learning (BERT/GPT)
• 5. Resume Parsing: Extract details using NLP
• 6. Matching Algorithm: Compare resumes with
job descriptions
Dataset Used
• - Source: Open-source resume datasets
(Kaggle, LinkedIn, GitHub)
• - Data Includes:
• • Candidate Name, Skills, Experience,
Education
• • Resume Text Content
• • Job Descriptions & Requirements
AI Models & Techniques
• - Natural Language Processing (NLP): NER, TF-
IDF, Word Embeddings
• - Deep Learning: BERT, GPT, LSTMs, CNNs
• - Machine Learning: Random Forest, SVM,
Logistic Regression
System Architecture
• - User Interface: Web Application (Frontend)
• - Backend Processing: Python Flask/Django API
• - Database: Stores resumes, feedback, job
descriptions
• - AI Processing: NLP & Deep Learning models
analyze data
• - Output: Resume ranking, feedback, and job
matching
Workflow Diagram
• Process Flow:
• 1. Upload Resume
• 2. Preprocessing
• 3. AI Analysis
• 4. Scoring
• 5. Feedback Generation
• (Visual representation recommended)
Expected Results & Benefits
• For Recruiters:
• ✔ Saves time in shortlisting candidates
• ✔ Increases accuracy in hiring decisions
• For Job Seekers:
• ✔ Personalized resume suggestions
• ✔ Higher chances of getting shortlisted
Future Scope & Enhancements
• - Multi-language support for global users
• - Integration with LinkedIn & job portals
• - AI-powered interview preparation
• - Voice-based resume input (Speech-to-Text)
Conclusion
• - AI Resume Analyzer automates resume
screening and job matching.
• - Helps HR teams find the best candidates
efficiently.
• - Provides job seekers with actionable
feedback for improvement.
Thank You!
• Contact Information: [Your Email, GitHub,
LinkedIn]
• Questions & Discussions

AI_Resume_Analyzer_Using_nlp_Presentation.pptx

  • 1.
    AI Resume Analyzer LeveragingNLP & Deep Learning for Intelligent Resume Screening By [Your Name] | [Date]
  • 2.
    Introduction • - AIResume Analyzer automates resume screening using AI. • - Helps companies find the best candidates efficiently. • - Assists job seekers in improving their resumes. • - Manual resume screening is time-consuming; AI ensures accuracy.
  • 3.
    Objectives • - Automateresume screening using AI & NLP. • - Provide resume ranking based on job requirements. • - Generate feedback & improvement suggestions. • - Improve hiring efficiency and job-seeker success.
  • 4.
    Features • For HR/ Recruiters: • ✔ Upload multiple resumes at once • ✔ AI-based ranking of resumes • ✔ Job-specific resume filtering • ✔ Automated shortlisting • For Job Seekers: • ✔ Instant resume feedback • ✔ Resume Score & Suggestions
  • 5.
    Technologies Used • -Frontend: HTML, CSS, JavaScript • - Backend: Python (Flask/Django) • - Database: PostgreSQL / MongoDB • - AI Models: NLP & Deep Learning • - Libraries: Spacy, NLTK, TensorFlow, PyTorch, OpenAI GPT / BERT
  • 6.
    Methodology (Step-by-Step) • 1.Data Collection: Gather resume datasets • 2. Preprocessing: Tokenization, Stop-word removal • 3. Feature Extraction: TF-IDF, Word Embeddings • 4. Model Training: Deep Learning (BERT/GPT) • 5. Resume Parsing: Extract details using NLP • 6. Matching Algorithm: Compare resumes with job descriptions
  • 7.
    Dataset Used • -Source: Open-source resume datasets (Kaggle, LinkedIn, GitHub) • - Data Includes: • • Candidate Name, Skills, Experience, Education • • Resume Text Content • • Job Descriptions & Requirements
  • 8.
    AI Models &Techniques • - Natural Language Processing (NLP): NER, TF- IDF, Word Embeddings • - Deep Learning: BERT, GPT, LSTMs, CNNs • - Machine Learning: Random Forest, SVM, Logistic Regression
  • 9.
    System Architecture • -User Interface: Web Application (Frontend) • - Backend Processing: Python Flask/Django API • - Database: Stores resumes, feedback, job descriptions • - AI Processing: NLP & Deep Learning models analyze data • - Output: Resume ranking, feedback, and job matching
  • 10.
    Workflow Diagram • ProcessFlow: • 1. Upload Resume • 2. Preprocessing • 3. AI Analysis • 4. Scoring • 5. Feedback Generation • (Visual representation recommended)
  • 11.
    Expected Results &Benefits • For Recruiters: • ✔ Saves time in shortlisting candidates • ✔ Increases accuracy in hiring decisions • For Job Seekers: • ✔ Personalized resume suggestions • ✔ Higher chances of getting shortlisted
  • 12.
    Future Scope &Enhancements • - Multi-language support for global users • - Integration with LinkedIn & job portals • - AI-powered interview preparation • - Voice-based resume input (Speech-to-Text)
  • 13.
    Conclusion • - AIResume Analyzer automates resume screening and job matching. • - Helps HR teams find the best candidates efficiently. • - Provides job seekers with actionable feedback for improvement.
  • 14.
    Thank You! • ContactInformation: [Your Email, GitHub, LinkedIn] • Questions & Discussions