Code-Alongs

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

To get the most out of your Code-Along experience, make sure you:

• Review the core competencies before coming to class
• Watch the guided projects before coming to class
• Take the checks for understanding before coming to class
• Finish your module projects before coming to class

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.

This code-along focuses on converting language data to numerical features, which is a crucial step in preparing text data for machine learning models.

Resources:

Starter Link | Solution Link

Topics Covered:

• Text preprocessing techniques
• Converting text to numerical features
• Feature extraction methods for text data
• Preparing text data for machine learning models

This code-along focuses on applying machine learning techniques to language data, building on the feature extraction methods from the previous code-along.

Resources:

Starter Link | Solution Link

Topics Covered:

• Building classification models for text data
• Evaluating model performance on language datasets
• Tuning machine learning models for NLP tasks
• Advanced NLP machine learning applications

Text Processing and Feature Extraction

• spaCy Linguistic Features Documentation
• Scikit-Learn: Text Feature Extraction
• NLTK Book: Processing Raw Text

Machine Learning for NLP

• Kaggle: Bag of Words Meets Bags of Popcorn Tutorial

Advanced Techniques

• Gensim: Word2Vec Tutorial
• TensorFlow: Word Embeddings