Natural Language Processing (NLP & Deep Learning)
Deepen your AI and data science knowledge by learning about deep neural networks and how to effectively utilize them in natural language processing.Enroll in Natural Language Processing (NLP & Deep Learning)
About This Online Course
Continue to expand your professional repertoire of artificial intelligence—particularly natural language processing—machine learning and data science principles, techniques and tools by taking this intermediate-level course from statistics.com on deep neural networks (or deep learning). You will learn how to leverage deep learning in processing, understanding and mining for insights from text.
We start the course with an introduction to neural networks and deep learning and then dive into the essentials of representation learning—for instance, word and document embeddings. We then dig a bit deeper into more complex methodologies, like convolutional neural networks and sequence models and deep transfer learning approaches, including universal embeddings and transformers.
Popular applications will be covered in the course that will emphasize hands-on tutorials and exercises, including text classification, information extraction, recommenders, search, summarization, translation and more.
This online course utilizes Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data, 1st edition (Apress, 2019), Sarkar, D. (available on Amazon). Learners must purchase the book before starting the course.
The book was chosen for its wealth of hands-on Python illustrations and code (the code of the illustrations is organized here).
What You Will Learn
- How to specify and run artificial neural networks and deep networks
- How deep networks represent words as binary vectors
- How to use recurrent neural networks for sequential learning (sequence-to-sequence modeling)
- How to use attention models to improve predictive performance
Dipanjan (DJ) Sarkar has led advanced analytics initiatives working with several Fortune 500 companies, like Applied Materials and Intel, and open-source organizations, like Red Hat (now IBM). He primarily works on leveraging data science, machine learning and deep learning to build large-scale intelligent systems.
In 2020, Mr. Sarkar was recognized as one of the "Top Ten Data Scientists in India," by a leading technology magazines and publishing houses, and, in 2019, as a Google developer expert in machine learning by Google. He holds his Master of Technology degree from IIIT Bangalore, India, with specializations in Data Science and Software Engineering, and his post graduate diploma in Machine Learning and Artificial Intelligence from Columbia University.
Mr. Sarkar is a published author and has written books on R, Python, machine learning, natural language processing and deep learning.
Who Should Take This Course
This course is designed for data scientists and aspiring data scientists who want to analyze text data and build AI and machine learning models that use text data.
You should be sufficiently familiar with Python to follow and use code examples like those shared in the statistics.com course, Introduction to Natural Language Processing and Text Mining, and the neural net material covered in the statistics.com course, Predictive Analytics 2 – Neural Nets and Regression.
A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.
In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.
This self-paced, online training course takes place at The Institute for Statistics Education at statistics.com for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. Participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.
At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.
$599 (per person)
Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.
Continuing Education Unit Credits
This online course provides 5.0 CEUs upon 50-percent completion.
This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.