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NVIDIA Generative AI LLMs (NCA-GENL) Exam Prep: Become a Certified Generative AI Specialist

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NVIDIA Generative AI LLMs (NCA-GENL) Exam Prep: Become a Certified Generative AI Specialist

Prepare to ace the NVIDIA Generative AI LLMs (NCA-GENL) Certification exam and earn your certification as a Generative AI Specialist. This comprehensive course is designed to equip you with the in-depth knowledge and practical skills needed to excel in the world of generative AI and large language models (LLMs), leveraging NVIDIA's cutting-edge technology.

What You'll Learn to Master the NCA-GENL Exam:

  • Machine Learning Fundamentals: Solidify your understanding of machine learning principles, algorithms, and techniques, crucial for grasping the inner workings of generative AI.

  • Deep Learning Fundamentals: Delve into deep learning architectures, neural networks, and training methodologies that empower LLMs to generate text, images, and other forms of content.

  • Generative AI and LLMs: Gain a deep understanding of generative AI concepts, model architectures (like transformers), and the unique capabilities of large language models.

  • Prompt Engineering: Master the art of prompt engineering, crafting precise and effective prompts to guide LLMs in producing desired outputs, from creative text generation to complex code synthesis.

  • Real-World Applications: Explore the diverse and transformative applications of generative AI across industries, including content creation, code generation, design, chatbots, and more.

  • NCA-GENL Exam Preparation: Receive targeted guidance and practice to confidently approach and pass the NVIDIA Generative AI LLMs (NCA-GENL) certification exam.

Is This Course Right for You?

This course is ideal for:

  • Developers seeking to integrate generative AI capabilities into their applications.

  • Data Scientists interested in harnessing the power of LLMs for text analysis, natural language processing, and data-driven insights.

  • Machine Learning Enthusiasts eager to explore the forefront of AI research, text generation, and language processing technologies.

  • AI Professionals aiming to enhance their skill set, advance their careers, and achieve the prestigious NVIDIA Generative AI with LLM Certification.

Prerequisites:

  • Basic programming experience (Python recommended)

  • Fundamental understanding of machine learning concepts

  • Access to a computer with internet connectivity for online learning

Enroll Now and Get Certified.

Prepare yourself for a rewarding career in generative AI. Gain the skills and knowledge to develop and deploy innovative AI solutions with NVIDIA's powerful technology. Pass the NCA-GENL exam with confidence and become a sought-after expert in the field.

Enroll now

What's inside

Learning objectives

  • Machine learning fundamentals
  • Deep learning fundamentals
  • Generative ai and llms
  • Nvidia gpu acceleration
  • Prompt engineering
  • Nca-genl exam preparation

Syllabus

Introduction
Welcome to the Course
Get Slide Resources - Source code
What makes this course Unique
Read more
Machine Learning Fundamentals
Introduction to Machine Learning Fundamentals

Here's the Quiz for Self Evaluation on Machine Learning Fundamentals

About Instructor
Introduction to Machine Learning
Types of Machine Learning
Linear Regression & Evaluation Metrics for Regression
Regularization and Assumptions of Linear Regression
Logistic Regression
Gradient Descent
Logistic Regression Implementation and EDA
Evaluation Metrics for Classification
Decision Tree Algorithms
Loss Functions of Decision Trees
Decision Tree Algorithm Implementation
Overfit Vs Underfit - Kfold Cross validation
Hyperparameter Optimization Techniques
KNN Algorithm
SVM Algorithm
Ensemble Learning - Voting Classifier
Ensemble Learning - Bagging Classifier & Random Forest
Ensemble Learning - Boosting Adabost and Gradient Boost
Emsemble Learning XGBoost
Clustering - Kmeans
Clustering - Hierarchial Clustering
Clustering - DBScan
Time Series Analysis
ARIMA Hands On
Fundamentals of Deep Learning
Deep Learning Fundaments - Introduction
Introduction to Deep Learning
Introduction to Tensorflow & Create first Neural Network
Intuition of Deep Learning Training
Activation Function
Architecture of Neural Networks
Deep Learning Model Training. - Epochs - Batch Size
Hyperparameter Tuning in Deep Learning
Vanshing & Exploding Gradients - Initializations, Regularizations
Introduction to Convolutional Neural Networks
Implementation of CNN on CatDog Dataset
Transfer Learning for Computer Vision
Feed Forward Neural Network Challenges
RNN & Types of Architecture
LSTM Architecture
Attention Mechanism
Transfer Learning for Natural Language Data
Essentials of NLP
Introduction to NLP Section
Introduction to NLP and NLP Tasks
Understanding NLP Pipeline
Text Preprocessing Techniques - Tokenization
Text Preprocessing - Pos Tagging, Stop words, Stemming & Lemmatization
Feature Extraction - NLP
One Hot Encoding Technique
Bag of Words & Count Vectorizer
TF IDF Score
Word Embeddings
CBoW and Skip gram - word embeddings
Large Language Models
Introduction to Large Language Models
How Large Language Models (LLMs) are trained
Capabilities of LLMs
Challenges of LLMs
Introduction to Transformers - Attention is all you need
Positional Encodings
Positional Encodings - Deep Dive
Self Attention & Multi Head Attention
Self Attention & Multi Head Attention - Deep Dive
Understanding Masked Multi Head Attention
Masked Multi Head Attention - Deep Dive
Encoder Decoder Architecture
Customization of LLMs - Prompt Engineering
Customization of LLMs - Prompt Learning - Prompt Tuning & p-tuning
Difference between Prompt Tuning and p-tuning
PEFT - Parameter Efficient Fine Tuning
Training data for LLMs
Pillars of LLM Training Data: Quality, Diversity, and Ethics
Data Cleaning for LLMs
Biases in Large Language Models
Loss Functions for LLMs
Prompt Engineering for the NCA-GENL Exam
What is Prompt Engineering ?
Advanced Prompt Engineering
Techniques for Effective Prompts
Ethical Considerations in Prompt Design for Large Language Models
NVIDIA's Tools and Frameworks for Prompt Engineering
NVIDIA Ecosystem tools for LLM Model Training
Data Analysis and Visualization
Data Visualization & Analysis of LLMs
EDA for LLMs
Experimentation
Experiment Design Principles for LLMs
Techniques for Large Language Models Experimentation
Data Management and Version Control for LLM experimentation
NVIDIA Ecosystem tools for LLM Experimentation, Data Management and Version Cont
LLM integration & Deployment

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Students interested in deep learning and LLM's will find this course to be right up their alley
Students interested in machine learning will find this course to be right up their alley
The way in which this course covers deep learning and LLM's makes it ideal for students who want to explore the forefront of AI research
The way in which this course covers deep learning and LLM's makes it ideal for students who want to advance their careers
Students interested in NLP will find this course to be right up their alley

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in NVIDIA-Certified Associate - Generative AI LLMs (NCA-GENL) with these activities:
Review Machine Learning Fundamentals
Reinforce your foundational understanding of machine learning principles, algorithms, and techniques to enhance your comprehension of generative AI.
Browse courses on Machine Learning Basics
Show steps
  • Revisit key concepts like supervised and unsupervised learning, regression and classification algorithms, and evaluation metrics.
  • Practice applying fundamental machine learning techniques to real-world datasets.
Organize course materials
Organize notes, assignments, quizzes, and exams to better understand course content
Show steps
  • Create a dedicated folder or notebook for course materials
  • File materials by topic or assignment
  • Review materials regularly to reinforce learning
Practice Prompt Engineering Techniques
Develop your skills in crafting effective prompts that guide LLMs to generate desired outputs, enhancing your ability to leverage generative AI capabilities.
Browse courses on Prompt Engineering
Show steps
  • Experiment with different prompt formats and structures.
  • Practice fine-tuning prompts to improve LLM responses.
  • Analyze and evaluate the outcomes of various prompts to optimize your approach.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Implementing Generative AI Techniques with Real-World Datasets
Enhance your practical skills by implementing generative AI techniques using real-world datasets, enabling you to effectively apply these technologies to solve real-world problems.
Show steps
  • Identify and gather real-world datasets that are relevant to the generative AI techniques you want to practice.
  • Clean and prepare the datasets for use in your implementations.
  • Apply various generative AI techniques to the datasets and analyze the results.
  • Evaluate the effectiveness of the techniques and identify areas for improvement.
Build a Generative AI Application Using NVIDIA's LLMs
Solidify your understanding and practical skills by building a generative AI application using NVIDIA's LLMs, demonstrating your ability to apply these technologies in real-world scenarios.
Show steps
  • Define the scope and objectives of your generative AI application.
  • Select appropriate NVIDIA LLM(s) and integrate them into your project.
  • Develop a user interface and workflow for your application.
  • Test and iterate on your application to optimize performance and user experience.
Develop a Generative AI Model for a Specific Industry
Demonstrate your expertise by developing a generative AI model tailored to a specific industry, showcasing your ability to apply these technologies to solve real-world problems.
Show steps
  • Identify a specific industry that could benefit from generative AI.
  • Research and understand the challenges and opportunities within the industry.
  • Design and develop a generative AI model that addresses a specific problem or need in the industry.
  • Evaluate the performance and impact of your generative AI model.

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Learners who complete NVIDIA-Certified Associate - Generative AI LLMs (NCA-GENL) will develop knowledge and skills that may be useful to these careers:

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