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Free AI Project Templates

Ready-to-Use AI Project Templates

Jump-start your AI development with professionally crafted project templates. Complete with source code, documentation, and best practices.

6 TemplatesFull Source CodeProduction Ready

Featured Templates

Beginner
Computer Vision

Image Classification with CNN

Build and train a Convolutional Neural Network to classify images using TensorFlow/Keras. Perfect for learning computer vision fundamentals.

Technologies:

TensorFlow
Python
OpenCV
NumPy
Size: 15 MB
Time: 4-6 hours
Intermediate
NLP

Sentiment Analysis API

Create a RESTful API that analyzes sentiment in text using NLTK and scikit-learn. Includes web interface and deployment configuration.

Technologies:

Flask
NLTK
scikit-learn
Docker
Size: 8 MB
Time: 6-8 hours
Advanced
Deep Learning

Stock Price Prediction

Predict stock prices using LSTM neural networks and technical indicators. Includes data fetching, preprocessing, and visualization.

Technologies:

PyTorch
Pandas
yfinance
Plotly
Size: 12 MB
Time: 8-10 hours

All Templates

Beginner
Computer Vision

Image Classification with CNN

Build and train a Convolutional Neural Network to classify images using TensorFlow/Keras. Perfect for learning computer vision fundamentals.

Key Features:

  • Pre-built CNN architecture
  • Data preprocessing pipeline
  • Training and validation scripts

Technologies:

TensorFlow
Python
OpenCV
NumPy
Size: 15 MB
Time: 4-6 hours
Intermediate
NLP

Sentiment Analysis API

Create a RESTful API that analyzes sentiment in text using NLTK and scikit-learn. Includes web interface and deployment configuration.

Key Features:

  • Text preprocessing pipeline
  • Multiple ML models comparison
  • RESTful API endpoints

Technologies:

Flask
NLTK
scikit-learn
Docker
Size: 8 MB
Time: 6-8 hours
Advanced
Deep Learning

Stock Price Prediction

Predict stock prices using LSTM neural networks and technical indicators. Includes data fetching, preprocessing, and visualization.

Key Features:

  • Real-time data fetching
  • LSTM model architecture
  • Technical indicators calculation

Technologies:

PyTorch
Pandas
yfinance
Plotly
Size: 12 MB
Time: 8-10 hours
Intermediate
Machine Learning

Movie Recommendation Engine

Build collaborative and content-based recommendation systems using the MovieLens dataset. Includes evaluation metrics and comparison.

Key Features:

  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommendation model

Technologies:

Python
Pandas
scikit-learn
Surprise
Size: 20 MB
Time: 5-7 hours
Advanced
NLP

AI Chatbot with NLU

Create an intelligent chatbot using natural language understanding, intent recognition, and response generation.

Key Features:

  • Intent classification
  • Entity extraction
  • Dialog management

Technologies:

Rasa
Python
SpaCy
TensorFlow
Size: 25 MB
Time: 10-12 hours
Intermediate
Data Science

ML Model Performance Dashboard

Interactive dashboard for monitoring machine learning model performance with real-time metrics and visualizations.

Key Features:

  • Model performance tracking
  • Interactive visualizations
  • Data drift detection

Technologies:

Streamlit
Plotly
MLflow
Pandas
Size: 10 MB
Time: 4-6 hours

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