Building Features from Text Data in Microsoft Azure

Unlock the power of text data for machine learning with Building Features from Text Data in Microsoft Azure. This course equips you with the skills to transform raw text into valuable features for building effective machine learning models.

You'll learn to structure your text data using Microsoft Azure Machine Learning Service virtual machines, gaining practical experience in data preparation. A core focus is natural language processing (NLP), where you'll master essential techniques for cleaning and preparing text. Discover how to effectively use:

• Document Tokenization: Breaking down text into individual units.
• Stopword Removal: Eliminating common words that add little meaning.
• Frequency Filtering: Focusing on the most relevant words.
• Stemming & Lemmatization: Reducing words to their root form for better analysis.
• Parts-of-Speech Tagging: Identifying the grammatical role of each word.
• N-gram Identification: Analyzing sequences of words.

Next, explore how to represent documents as numerical text features. You'll learn various encoding methods, including:

• One-Hot & Count Vector Encodings: Traditional methods for representing text.
• Frequency-Based Encodings: Leveraging word frequencies for feature creation.
• Word Embeddings: Representing words as dense vectors capturing semantic meaning.
• Hashing: Efficiently mapping text to feature vectors.
• Locality-Sensitive Hashing: Finding similar documents based on their feature vectors.

Finally, delve into advanced techniques using BERT (Bidirectional Encoder Representations from Transformers) to generate powerful word embeddings, capturing contextual information for improved model performance.

By the end of this course, you'll be proficient in utilizing textual data and the capabilities of Microsoft Azure to create robust and effective text features for a wide range of machine learning applications. Learn to make data-driven decisions by effectively leveraging the power of text.