Look inside
In this liveProject, you’ll use the Python tools pandas and Folium to prepare the Airbnb dataset for training a deep learning model. You’ll learn Python concepts that ensure the application you build on this data is robust and maintainable, and implement geospatial and visualization techniques to illustrate the geographic distribution of Airbnb rentals.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
book resources
When you start your liveProject, you get full access to the following books for 90 days.
project author
Mark Ryan
Mark Ryan has a Bachelor in Computer Science from the University of Waterloo and an MSc in Computer Science (with a specialty in Artificial Intelligence) from the University of Toronto. As a senior leader for IBM's Db2 relational database, he was immersed in structured, tabular data. Mark is currently a Data Science Manager at Intact Insurance. In addition to deep learning, Mark's interests include chatbots and transformer-based language models.
prerequisites
This liveProject is for intermediate data scientists looking to expand their skill set with applied deep learning capabilities. To begin this liveProject, you will need to be familiar with:
TOOLS
- Intermediate Python
- Basics of pandas
- Basics of Folium
- Basics of Flask
- Basics of scikit-learn
- Basics of Keras
- Basics of Ngrok
- Basics of Google Colab
- Basics of Jupyter Notebook
- Basics of HTML, CSS, and JavaScript
TECHNIQUES
- Deep learning model layer assembly
- Deep learning model training
- Trained deep learning model one-off scoring
- Invoking deep learning models
- Basics of web development
you will learn
In this liveProject, you’ll master all the skills you need to build a complete deep learning model and deploy it to production. You’ll get to grips with a common Python stack used for deep learning across numerous companies and industries.
- Manipulate tabular data
- Make geospatial visualizations
- Use Keras to interface with TensorFlow
- Adapt pipeline classes to encapsulate data preparation steps
- Utilize Keras callbacks
- Track and assess model performance
- Create a web server with Flask
- Serve deployment web pages
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.