This course will teach you how machines can be trained to understand and process human language using various NLP algorithms. You'll explore lexical processing, basic syntactic processing, and mechanisms like those used by Google Translator to grasp language context and translation. One hands-on project involves building a chatbot with Rasa, which handles text- and voice-based conversations, connects to messaging channels, and integrates APIs.

You'll also learn to train your models on natural language understanding (NLU). Traditional hand-coded programs fail to handle changing inputs, so this course focuses on creating models that understand context and adapt. Even if you lack prior knowledge of machine learning and deep learning, the course covers all necessary prerequisites. By the end, you'll be proficient in building NLP models for text summarization, sentiment analysis, and entity recognition, all through real-world projects.

This course is ideal for students entering data science, professionals familiar with deep learning, and developers interested in creating chatbots or working on Alexa and Google Home projects.

Applied Learning Project

The projects in course will provide hands-on experience with NLP techniques, enabling learners to apply skills in authentic scenarios such as text data analysis, syntactic and semantic processing, and building models for tasks like spam detection and information extraction. By completing these projects, learners will gain practical expertise to solve real-world problems using NLP.