In the world of Natural Language Processing (NLP), Hugging Face has emerged as a powerful platform that democratizes access to state-of-the-art NLP models and tools. This comprehensive course, “Hugging Face for Natural Language Processing (NLP) Training,” is designed to equip you with the knowledge and skills needed to harness the full potential of Hugging Face’s transformers library and related resources for a wide range of NLP tasks. Whether you’re a data scientist, machine learning engineer, or NLP enthusiast, this course will empower you to leverage cutting-edge models and techniques effectively.
Prerequisites
Basic knowledge of Python programming.
Familiarity with machine learning and deep learning concepts is helpful but not mandatory.
No prior experience with Hugging Face is required, as the course covers the platform comprehensively.
What will you gain after this course
Introduction to Hugging Face: Gain a foundational understanding of Hugging Face, its mission, and its role in the NLP landscape.
Exploration of Transformers: Dive into the transformers library, explore pre-trained models, and learn how to fine-tune them for specific NLP tasks.
NLP Fundamentals: Build a strong foundation in NLP concepts, including tokenization, word embeddings, attention mechanisms, and more.
Hands-on Model Training: Develop the skills to train and fine-tune NLP models on custom datasets using Hugging Face’s easy-to-use API.
Transfer Learning: Understand the power of transfer learning and how to adapt pre-trained models to your specific NLP applications.
Model Deployment: Learn how to deploy Hugging Face models in various production environments, including web applications and cloud platforms.
Advanced NLP Tasks: Explore advanced NLP tasks such as text generation, question answering, sentiment analysis, and language translation using Hugging Face models.
Model Interpretability: Discover techniques for interpreting and explaining the predictions of NLP models, including attention visualization and saliency maps.
Real-world Applications: Apply your NLP skills to real-world projects, including chatbots, sentiment analysis systems, and text summarization applications.
Jobs you can get with a Natural Language Processing (NLP) Fundamentals with Python Certification
Natural Language Processing Specialist
Senior Data Scientist – Natural Language Processing
Application Developer – Natural Language Processing
Data Language Engineer
Senior Associate, Analytics – Natural Language Processing
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Who is this certification for?
Data Scientists and Analysts
Machine Learning Engineers
NLP Practitioners
Software Developers interested in NLP
Anyone looking to enhance their NLP skills using Hugging Face
Small Groups
With small groups of students, our instructors can work closely with each student.
Flexible Class Schedules
Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.
Experienced Instructors
Our instructors follow a modified are personalized approach to engage students during class
Hi-Tech Lab Facilities
Our students can access our lab facilities anytime for practical experience during and after studies.
Syllabus
Introduction
Overview of NLP and its applications
Introduction to Hugging Face and its key features
Setting up a working environment
Installing and configuring Hugging Face
Understanding the Hugging Face Transformers library and Transformer Models
Exploring the Transformers library structure and functionalities
Overview of various Transformer models available in Hugging Face
Utilizing Hugging Face Transformers
Loading and using pretrained models
Applying Transformers for various NLP tasks
Fine-Tuning a Pretrained Model
Preparing a dataset for fine-tuning
Fine-tuning a Transformer model on a specific task
Sharing Models and Tokenizers
Exporting and sharing trained models
Utilizing tokenizers for text processing
Exploring Hugging Face Datasets Library
Overview of the Datasets library in Hugging Face
Accessing and utilizing pre-existing datasets
Exploring Hugging Face Tokenizers Library
Understanding tokenization techniques and their importance
Leveraging tokenizers from Hugging Face
Carrying out Classic NLP Tasks
Implementing common NLP tasks using Hugging Face
Text classification, sentiment analysis, named entity recognition, etc.
Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision
Extending the use of Transformers beyond text-based tasks
Applying Transformers for speech and image-related tasks
Troubleshooting and Debugging
Common issues and challenges in working with Hugging Face
Techniques for troubleshooting and debugging
Building and Sharing Your Model Demos
Designing and creating interactive model demos
Sharing and showcasing your models effectively
Summary and Next Steps
Recap of key concepts and techniques learned
Guidance on further exploration and resources for continued learning
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