What Is the PL-300 Exam?
April 5, 2024
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Launch career in NVIDIA Generative AI with LLMs. Master AI, ML, and Deep Learning using NVIDIA tools.
Instructor: Whizlabs Instructor
Included with
Recommended experience
Intermediate level
A basic understanding of generative AI and large language models.
Knowledge of Python, C, and AI frameworks (PyTorch, TensorFlow, etc.).
Recommended experience
Intermediate level
A basic understanding of generative AI and large language models.
Knowledge of Python, C, and AI frameworks (PyTorch, TensorFlow, etc.).
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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February 2025
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NVIDIA-Certified Generative AI LLMs - Associate Specialization is intended for candidates working in AI, machine learning, and deep learning roles who want to enhance their expertise in generative AI and large language models (LLMs). This specialization will prepare learners to design, build, optimize, and deploy generative AI solutions using NVIDIA technologies. If you're seeking a role in AI development, research, or cloud-based AI solutions, this focused course will equip you with the necessary skills and knowledge to become NVIDIA Generative AI LLMs - Associate certified.
Understanding of generative AI and LLMs.
Fundamental machine learning and deep learning concepts.
Knowledge of natural language processing (NLP) and transformer-based models.
LLM deployment strategies and prompt engineering techniques.
Ethical considerations in AI development and deployment.
This specialization is divided into a set of 6 courses covering the domain requirements for appearing in the NVIDIA-Certified Generative AI LLMs - Associate certification. The course details are as follows:
Course 1: NVIDIA: Fundamentals of Machine Learning
Course 2: NVIDIA: Fundamentals of Deep Learning
Course 3: NVIDIA: NLP: From Foundations to Transformers
Course 4: NVIDIA: Large Language Models and Generative AI Deployment
Course 5: NVIDIA: Prompt Engineering and Data Analysis
Course 6: NVIDIA: LLM Experimentation, Deployment, and Ethical Considerations
Applied Learning Project
Learners will be provided with Lab Demonstrations based on the topics discussed in the courses that are part of this specialization. They will also have the opportunity to understand the demo labs. However, these demo hands-on labs are optional for learners to perform.
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.
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.
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.
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.
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.
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.
Providing certification training since the year 2000, Whizlabs is the pioneer among online training providers across the globe. We are dedicated to helping you learn the skills you need to transform your career in the IT industry. We provide certification training in the form of Video Courses, Practice Tests, Hands-on Labs and Sandbox in various disciplines such as Cloud Computing, DevOps, Cyber Security, Java, Big Data, Snowflake, CompTIA, Agile, Linux, CCNA, Blockchain, and much more.
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The specialization will be completed in 4 months
Basic understanding of generative AI and large language models, along with knowledge of Python, C, and AI frameworks like PyTorch and TensorFlow.
The courses can be taken in any order.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.