TrueschoTruescho
All Courses
Applied Natural Language Processing in Engineering Part 2
Coursera
Course
Unknown

Applied Natural Language Processing in Engineering Part 2

Northeastern University

Advanced course for building and deploying NLP systems using recurrent neural networks, transformers, and practical frameworks like PyTorch and Hugging Face.

Unknown7 weeksEnglish, HU

About this Course

This course is best suited for software engineers, data scientists, and graduate students in computer science or engineering fields who wish to develop expertise in building and deploying natural language processing systems to solve real-world language understanding challenges. You will master core NLP tasks such as Part-of-Speech tagging, Named Entity Recognition, sentiment analysis, and Neural Machine Translation while implementing various neural architectures from Recurrent Neural Networks and bidirectional RNNs to Conditional Random Fields and state-of-the-art transformer models. The course emphasizes practical application through extensive laboratory work and projects, where you will develop complete NLP pipelines using frameworks like PyTorch and Hugging Face, learning to preprocess data, train models, and evaluate performance using industry-standard metrics. By the end of the course, you will be equipped with both theoretical understanding and practical skills to design, implement, and optimize NLP solutions for real-world engineering applications, from chatbots and translation systems to information extraction and text analysis tools. The curriculum culminates in a comprehensive capstone project where you will apply multiple techniques learned throughout the course to solve a complex language processing challenge. You will be equipped with both theoretical knowledge to tackle complex language processing problems in industry settings, enabling you to build production-ready NLP applications that can understand, interpret, and generate human language effectively

What You'll Learn

  • Develop advanced NLP system building skills
  • Master core NLP tasks including POS tagging, Named Entity Recognition, sentiment analysis
  • Implement recurrent neural networks, conditional random fields, and transformer models
  • Build complete NLP pipelines using PyTorch and Hugging Face frameworks

Prerequisites

  • Basic computer and internet skills
  • Ability to read course instructions and complete short practice activities

Instructors

R

Ramin Mohammadi

Topics

Machine Learning
Data Science
Natural Language Processing
Large Language Modeling
Deep Learning
Machine Learning Methods
Transfer Learning
Hugging Face
Applied Machine Learning
Embeddings

Course Info

PlatformCoursera
LevelUnknown
PacingUnknown
PriceFree

Skills

التعلم الآلي
علوم البيانات
معالجة اللغات الطبيعية
نماذج اللغة الكبيرة
التعلم العميق
طرق التعلم الآلي
التعلم بالنقل
Hugging Face
Applied Machine Learning
Embeddings

Start Learning Now