CTECH467 - Using Machine Learning for Text Analysis

Course Description

Natural language processing (NLP) is a branch of artificial intelligence that helps machines process and understand human language in speech and text form. In order for machine learning models to process words and blocks of text, the text must first be transformed into numerical features. There are various NLP preprocessing techniques that accomplish this.

In this course, you will explore these techniques and the typical workflow for converting text data for NLP. You will also use a special scikit-learn utility that allows you to automate the workflow as a pipeline. At the end of the course, you will have the opportunity to explore neural networks, powerful ML models that are heavily used in the field of NLP. You will also discover different Python packages used to construct neural networks and see how to implement a feedforward neural network using Keras. You will then delve into deep neural networks, which are used to solve large-scale complex problems, and you will implement a deep neural network for sentiment analysis. By the end of this course, you will have a foundation in using ML for text analysis relevant to limitless real-life applications.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Machine Learning Foundations
• Managing Data in Machine Learning
• Training Common Machine Learning Models
• Training Linear Models
• Evaluating and Improving Your Mode
• Improving Performance With Ensemble Methods

Faculty Author

Benefits to the Learner

• Use various NLP preprocessing techniques to convert text to data suitable for machine learning
• Understand how word embeddings are used to convert text into numerical features without losing the underlying semantic meaning
• Implement ML models that make predictions from text data
• Explore feedforward neural networks
• Discover how deep neural networks are used in the NLP field
• Implement a feedforward neural network for sentiment analysis

Target Audience

• Data scientists and data analysts
• Programmers, developers, and software engineers
• Statisticians
• Product managers
• Entrepreneurs
• Working professionals seeking to upskill or career change

Applies Towards the Following Certificates

• Applied Machine Learning and AI : Core Courses