Hugging Face Transformersin Los Angeles

40 Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Los Angeles, United States. Dates, seating capacity, facilities and the final venue are confirmed before booking.

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Los Angeles is known for diverse industries, professional networks and internationally connected business communities.

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Industry, innovation and professional networks

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Explore Hugging Face Transformers

Explore Hugging Face Transformers training to learn transformer architecture, pretrained models, tokenization, natural language processing, and fine-tuning techniques. Gain practical experience building AI applications for text classification, summarization, translation, and text generation. Develop skills in Python, model integration, and working with the Hugging Face ecosystem. Join StepMerit's Hugging Face Transformers course in Los Angeles, United States, to strengthen your machine learning and Generative AI development expertise.

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Learning from Los Angeles

Los Angeles is known for diverse industries, professional networks and internationally connected business communities.

Course curriculum

Module 1: Introduction to Hugging Face Transformers
- Overview of Hugging Face and its AI ecosystem.
- Understanding transformer-based models and their applications.
- Exploring the Hugging Face Hub and model repositories.
- Benefits of using Hugging Face Transformers for AI development.
- Introduction to the course and StepMerit training in Los Angeles, United States.
Module 2: Fundamentals of Transformer Architecture
- Understanding transformer models and their structure.
- Introduction to attention mechanisms and self-attention.
- Understanding encoders, decoders, and encoder-decoder architectures.
- Exploring embeddings, positional encoding, and model layers.
Module 3: Setting Up the Development Environment
- Installing Python and the Hugging Face Transformers library.
- Configuring Jupyter Notebook, Google Colab, or a local environment.
- Installing supporting libraries such as PyTorch and Datasets.
- Managing dependencies and verifying the development setup.
Module 4: Working with the Hugging Face Hub
- Navigating model and dataset repositories.
- Understanding model cards, licences, and supported tasks.
- Downloading and loading pretrained models.
- Managing model versions and selecting suitable checkpoints.
Module 5: Tokenization and Text Preprocessing
- Understanding tokens, vocabularies, and token IDs.
- Working with pretrained tokenizers.
- Applying padding, truncation, and attention masks.
- Preparing text inputs for transformer models.
Module 6: Using Pretrained Models and Pipelines
- Understanding the Transformers pipeline API.
- Performing sentiment analysis and text classification.
- Implementing summarization and translation tasks.
- Exploring question answering and zero-shot classification.
- Comparing pipeline-based and custom model workflows.
Module 7: Text Generation and Language Models
- Loading causal language models for text generation.
- Configuring generation parameters such as temperature and top-k.
- Understanding sampling, greedy decoding, and beam search.
- Applying generation models to content creation and conversational tasks.
Module 8: Text Classification and Natural Language Processing
- Building text classification workflows.
- Exploring sentiment analysis and intent detection.
- Understanding sequence classification and model outputs.
- Evaluating classification predictions using appropriate metrics.
Module 9: Sequence-to-Sequence Models
- Understanding encoder-decoder transformer models.
- Working with text summarization models.
- Implementing machine translation workflows.
- Exploring text transformation and question-answering applications.
Module 10: Working with Datasets
- Introduction to the Hugging Face Datasets library.
- Loading datasets from repositories and local files.
- Cleaning, filtering, and transforming datasets.
- Splitting datasets into training, validation, and testing sets.
- Preparing datasets for model training and evaluation.
Module 11: Fine-Tuning Pretrained Models
- Understanding transfer learning and fine-tuning.
- Preparing datasets and tokenized inputs for training.
- Configuring training arguments and the Trainer API.
- Training models for task-specific requirements.
- Saving, loading, and managing fine-tuned models.
Module 12: Model Evaluation and Performance Optimisation
- Selecting suitable evaluation metrics for different tasks.
- Analysing model predictions and error patterns.
- Understanding overfitting and generalisation.
- Exploring batch processing, memory management, and inference optimisation.
- Considering hardware requirements and resource constraints.
Module 13: Parameter-Efficient Fine-Tuning
- Introduction to parameter-efficient fine-tuning techniques.
- Understanding LoRA and related adaptation methods.
- Exploring compatible PEFT workflows.
- Comparing full fine-tuning with parameter-efficient approaches.
- Identifying use cases where resource-efficient training is beneficial.
Module 14: Model Integration and Deployment
- Integrating transformer models into Python applications.
- Creating inference workflows for application use.
- Exploring API-based model serving and application integration.
- Understanding model loading, latency, and scalability considerations.
- Reviewing security, licence, and responsible-use considerations.
Module 15: Capstone Project and Practical Implementation
- Select a real-world NLP or text-generation problem.
- Identify a suitable pretrained model and dataset.
- Implement a complete preprocessing and inference workflow.
- Fine-tune or customise a model where appropriate.
- Evaluate results and document limitations.
- Present the final implementation and demonstrate the working application.

Exam details

Exam Level: Beginner to Intermediate – Hugging Face Transformers
Exam Type: Online computer-based assessment consisting of multiple-choice questions and scenario-based questions.
Number of Questions: 40 questions.
Exam Duration: 90 minutes.
Passing Score: 70% (28 out of 40 questions).
Assessment Platform: StepMerit online assessment platform.
Practical Assessment: May include exercises involving pretrained model usage, tokenization, pipeline configuration, dataset preparation, or model fine-tuning.
Evaluation Criteria: Assessment of conceptual understanding, practical application, model selection, NLP workflows, and basic troubleshooting skills.
Certification: Learners who successfully complete the assessment may receive a StepMerit Certificate of Course Completion. This is not an official Hugging Face certification.
Exam Preparation: Review transformer architecture, Hugging Face Hub, tokenizers, pretrained models, pipelines, datasets, fine-tuning, and model evaluation. Practise implementing common NLP tasks using Python.
Training Location: StepMerit offers Hugging Face Transformers training in Los Angeles, United States, helping learners develop practical skills in transformer-based AI application development.

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Who this course is for

Python Developers Machine Learning Engineers Natural Language Processing (NLP) Professionals Generative AI Developers Artificial Intelligence Engineers Data Scientists Deep Learning Practitioners Software Developers AI Researchers and Students IT Professionals and Aspiring AI Developers seeking Hugging Face Transformers training in Los Angeles, United States

Before you begin

Basic Python Programming: Familiarity with Python syntax, functions, classes, lists, dictionaries, and libraries. Machine Learning Fundamentals: Basic understanding of supervised learning, unsupervised learning, model training, and evaluation. Deep Learning Knowledge: Familiarity with neural networks, training processes, and fundamental deep learning concepts is recommended. Natural Language Processing (NLP): Basic knowledge of text preprocessing, tokenisation, text classification, and language modelling is beneficial. Transformer Architecture: An introductory understanding of attention mechanisms, encoders, decoders, and transformer-based models is helpful but not mandatory. Python…

Learning outcomes

Problem Statement: Educational organisations manage large volumes of learning materials, course notes, textbooks, assignments, student queries, and academic documents. Traditional search and manual content processing can make it difficult for students to find relevant information quickly. Teachers also spend time summarising lengthy materials, categorising student feedback, and preparing learning resources. The organisation wanted to use transformer-based AI models to improve educational content discovery and automate common text-processing tasks. How We Overcame the Problem: Learners designed an AI-powered educational text-processing solution using Hugging Face Transformers and pretrained…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional Experience: 5 years of experience in Python development, Machine Learning, Natural Language Processing (NLP), and AI application development. Course-Specific Expertise: Practical knowledge of Hugging Face Transformers, pretrained models, transformer architecture, tokenization, text classification, summarisation, and model fine-tuning. Training Experience: 100+ hours of technical training delivered in Python, Machine Learning, NLP, and transformer-based AI technologies. Candidates Trained: 150+ candidates trained in Python programming, Machine Learning, NLP fundamentals, and AI application development. Hands-on Training: Experience guiding learners through practical exercises…

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Insights for Hugging Face Transformers

Learning insight 01

Hugging Face Transformers: A Complete Guide to NLP and AI Model Development

Artificial Intelligence (AI) and Natural Language Processing (NLP) are transforming how organisations process information, automate tasks, and develop intelligent applications. From chatbots and text summarisation to language translation and content generation, transformer-based models are powering many modern AI…

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