🧠 AI Project Ideas for Intermediate Learners

1. AI-Powered Resume Screener

What: Automatically filter and rank resumes based on job descriptions.

Skills: NLP, keyword extraction, similarity scoring (TF-IDF or BERT).

Tools: Python, spaCy, sklearn, transformers

2. Fake News Detector

What: Detect whether a news article is real or fake.

Skills: NLP classification, text cleaning, deep learning.

Tools: LSTM/GRU with Keras, Hugging Face Transformers

3. Object Detection System

What: Detect and label multiple objects in images (like cars, people, etc.).

Skills: Computer vision, YOLO or SSD, image preprocessing.

Tools: OpenCV, TensorFlow, YOLOv5

4. Speech Emotion Recognition

What: Detect emotions (happy, sad, angry, etc.) from voice recordings.

Skills: Audio processing, feature extraction (MFCC), deep learning.

Tools: librosa, TensorFlow/Keras, PyDub

5. AI Chatbot with Context Memory

What: A chatbot that remembers previous user inputs to maintain a conversation.

Skills: NLP, session memory, language modeling.

Tools: Rasa, GPT-3.5, LangChain, Flask

6. Image Caption Generator

What: Generate a caption for an input image.

Skills: CNN + RNN combo, encoder-decoder architecture.

Tools: TensorFlow, Keras, MS COCO dataset

7. Autonomous Lane Detection

What: Detect lanes in road videos (used in self-driving cars).

Skills: Computer vision, edge detection, perspective transformation.

Tools: OpenCV, Python

8. Hand Gesture Recognition

What: Recognize hand signs (like ASL) using a webcam.

Skills: Real-time image classification, CNN.

Tools: OpenCV, Mediapipe, TensorFlow

9. Personalized News Recommender

What: Recommend news articles based on user behavior.

Skills: Collaborative filtering, NLP for content-based filtering.

Tools: Scikit-learn, Pandas, NLTK

10. AI Music Generator

What: Generate short melodies using neural networks.

Skills: Sequence modeling, RNN/LSTM, music theory basics.

Tools: Magenta, MuseNet, TensorFlow

🔧 Bonus: Tools to Explore

Hugging Face Transformers – For cutting-edge NLP models.

Gradio / Streamlit – For creating simple AI web apps.

Google Colab – Free GPU to train heavier models.

Docker – For containerizing and deploying models.

📚 Pro Tips for Intermediate Projects

Focus on clean, well-documented code.

Use version control (like Git and GitHub).

Learn to evaluate models properly (accuracy, F1, ROC-AUC).

Try deploying your model using Flask, FastAPI, or Streamlit.

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