Ementorhub

What you'll learn

Understand NLP fundamentals, key tasks, and real-world applications.

Implement NLP techniques, including tokenization, word embeddings, and sequence models.

Explore transformer architecture, self-attention mechanisms, and encoder-decoder models.

Skills you'll gain

Data Processing

Artificial Intelligence and Machine Learning (AI/ML)

Natural Language Processing

Data Cleansing

Text Mining

Artificial Neural Networks

Deep Learning

Machine Learning Methods

Applied Machine Learning

There are 2 modules in this course

This course covers key NLP topics, including tokenization, text preprocessing techniques, and word embeddings, along with the challenges of handling textual data. Learners will also explore sequence models (RNN, LSTM, GRU) and transformer architectures, gaining practical insights into self-attention mechanisms and encoder-decoder models. The course is structured into two modules, each comprising Lessons and Video Lectures. Learners will engage with approximately 3:00-3:30 hours of video content, covering both theoretical foundations and hands-on practice. Each module includes quizzes to reinforce learning and assess understanding. Course Modules: Module 1: Introduction to NLP: Concepts, Techniques, and Applications Module 2: Sequence Models and Transformers By the end of this course, a learner will be able to: - Understand NLP fundamentals, key tasks, and real-world applications. - Implement NLP techniques, including tokenization, word embeddings, and sequence models. - Explore transformer architecture, self-attention mechanisms, and encoder-decoder models. This course is intended for individuals interested in developing NLP expertise and working with transformer-based models. It is ideal for data scientists, machine learning engineers, and AI specialists seeking hands-on experience in modern NLP techniques.

Sequence Models and Transformers