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