Code-Alongs

What is a Code-Along?

Code-Alongs are live experiences taught by our instructors designed to prepare you for concepts found in the sprint challenges. They're your opportunity to work on complex job-ready problems in a live and engaging environment.

These sessions are 50 minutes in length and are offered seven days a week in the morning, afternoon, and evening. Because Code-Alongs delve deeper into a core competency, you will need to come to class prepared to have the best experience.

Ideal Code-Along Preparation Checklist

  • Did you review the core competencies?
  • Did you watch the guided projects?
  • Did you finish your module projects?

Code-Along 1: Language Data to Numerical Features

This code-along focuses on converting language data to numerical features, reinforcing concepts from Modules 1 and 2. You'll work through realistic examples of text preprocessing and feature extraction techniques essential for NLP applications.

Starter Repository Solution Repository

How to Prepare

  • Review text preprocessing concepts from Module 1
  • Review vector representation concepts from Module 2
  • Watch the guided project videos
  • Complete your module projects before attending

Code-Along 2: Machine Learning Application on Language Data

This session focuses on applying machine learning techniques to language data, providing hands-on practice with document classification and topic modeling from Modules 3 and 4.

Starter Repository Solution Repository

How to Prepare

  • Review document classification concepts from Module 3
  • Review topic modeling concepts from Module 4
  • Watch the guided project videos
  • Complete your module projects before attending

Prepare for Success

The best Code-Along experiences happen when you are ready before coming to class. Your instructors created a starting point and a solution for each of your Code-Alongs to ensure you have what you need to succeed.

Make sure to review the relevant module materials before attending the code-along. This will help you get the most out of the session and be better prepared to tackle the challenges.

Additional Resources

Text Processing and Feature Extraction

Machine Learning for NLP