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Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs Specialization
Whizlabs

Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs Specialization

Launch career in NVIDIA Generative AI with LLMs. Master AI, ML, and Deep Learning using NVIDIA tools.

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 months
at 3 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 months
at 3 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Validating your expertise in generative AI, LLMs, and deep learning techniques.

  • Gaining industry recognition for your AI and machine learning skills.

  • Enhancing career opportunities in AI research, development, and cloud-based AI solutions.

  • Positioning yourself as a specialist in cutting-edge AI technologies.

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Taught in English
Recently updated!

February 2025

Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Whizlabs
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Specialization - 6 course series

What you'll learn

  • Understand the fundamentals of AI, ML, and Deep Learning, and their key differences.

  • Implement supervised learning techniques like classification and regression.

  • Apply clustering methods and time series analysis using ARIMA.

  • Leverage NVIDIA RAPIDS for GPU-accelerated ML workflows.

Skills you'll gain

Category: Supervised Learning
Category: Unsupervised Learning
Category: Machine Learning Algorithms
Category: Machine Learning
Category: Deep Learning
Category: Feature Engineering
Category: Time Series Analysis and Forecasting
Category: Artificial Intelligence
Category: Regression Analysis
Category: Data Processing
Category: Classification And Regression Tree (CART)

What you'll learn

  • Understand deep learning fundamentals, including neuron data processing and model training.

  • Implement multi-class classification and CNNs for image recognition tasks.

  • Apply transfer learning with pre-trained models to improve deep learning performance.

Skills you'll gain

Category: Deep Learning
Category: Artificial Neural Networks
Category: Machine Learning Algorithms
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Network Architecture
Category: Computer Vision
Category: Network Model
Category: Supervised Learning
Category: Applied Machine Learning
Category: Image Analysis

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

Category: Natural Language Processing
Category: Data Pipelines
Category: Artificial Neural Networks
Category: Machine Learning
Category: Text Mining
Category: Deep Learning
Category: Data Processing
Category: Network Architecture

What you'll learn

  • Understand the foundational concepts of LLMs, including NLP and training data.

  • Explore model optimization techniques like loss functions, alignment, and PEFT.

  • Implement deployment strategies for LLMs and monitor performance using ONNX.

Skills you'll gain

Category: Generative AI
Category: Application Deployment
Category: Tensorflow
Category: Machine Learning
Category: Natural Language Processing
Category: Reinforcement Learning
Category: MLOps (Machine Learning Operations)
Category: PyTorch (Machine Learning Library)
Category: System Monitoring
Category: Deep Learning
Category: Data Processing
Category: Artificial Intelligence
Category: Data Cleansing

What you'll learn

  • Understand prompt engineering and its role in LLM optimization.

  • Apply P-tuning and RAG architecture for improved model performance.

  • Utilize data analysis and visualization techniques for effective NLP tasks.

Skills you'll gain

Category: Generative AI
Category: Histogram
Category: Natural Language Processing
Category: Artificial Intelligence
Category: Text Mining
Category: Performance Tuning
Category: Data Visualization Software
Category: Plot (Graphics)
Category: PyTorch (Machine Learning Library)
Category: Data Analysis
Category: Box Plots
Category: Tensorflow
Category: Scatter Plots

What you'll learn

  • Experiment with LLMs using hyperparameter tuning and A/B testing.

  • Apply version control and optimize AI workflows with NVIDIA tools like BioNeMo, Triton, and TensorRT.

  • Understand ethical AI principles, data privacy, and methods to minimize bias and enhance AI trustworthiness.

Skills you'll gain

Category: Version Control
Category: A/B Testing
Category: Data Ethics
Category: Deep Learning
Category: Information Privacy
Category: MLOps (Machine Learning Operations)
Category: Personally Identifiable Information
Category: Natural Language Processing
Category: Generative AI
Category: Scalability
Category: Application Deployment
Category: Performance Tuning
Category: Artificial Intelligence

Instructor

Whizlabs Instructor
Whizlabs
74 Courses60,667 learners

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