Deep learning for NLP using Python

Course details

In this course, youll expand your NLP knowledge and skills while implementing deep learning tools to perform complex tasks. Youll start by preparing your environment for NLP and then quickly learn about language structure and how we can break sentences down to extract information and uncover the underlying meaning. After reviewing the basics, well move on to speech recognition and show how deep learning can be used to build speech recognition applications.

In order to give you the best hands-on experience, the course includes a wide variety of practical real world examples. Youll discover how a Naive Bayes algorithm can be used for Binary and Multiclass text classification. Well show you how a binary classifier can be used to determine if a product review would best be classified as positive or negative. Youll also learn how document classifiers can be used to predict information about the author of a text like their age, gender, or where theyre from.

Finally speech recognition systems will be introduced and youll learn how to apply deep learning techniques to build your own speech to text application. Well walk through two examples, step-by-step, showing how to build and train neural networks to understand spoken audio inputs.

By the end of this tutorial, youll have a better understanding of NLP and will have worked on multiple examples that implement deep learning to solve real-world spoken language problems. In particular, youll be able to discover useful information and extract key insights from piles of natural language data. All the code and supporting files for this course are available on Github.

About the Author

Tyler Edwards is a senior engineer and software developer with over a decade of experience creating analysis tools in the space, defense, and nuclear industries. Tyler is experienced using a variety of programming languages (Python, C++, and more), and his research areas include machine learning, artificial intelligence, engineering analysis, and business analytics. Tyler holds a Master of Science degree in Mechanical Engineering from Ohio University. Looking forward, Tyler hopes to mentor students in applied mathematics, and demonstrate how data collection, analysis, and post-processing can be used to solve difficult problems and improve decision making.