NVIDIA-Certified Associate - Generative AI LLMs (NCA-GENL) from Udemy

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.

What's inside

Learning objectives

Machine learning fundamentals
Deep learning fundamentals
Generative ai and llms

Nvidia gpu acceleration
Prompt engineering
Nca-genl exam preparation

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

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

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

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Reinforce your foundational understanding of machine learning principles, algorithms, and techniques to enhance your comprehension of generative AI.

Browse courses on Machine Learning Basics

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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

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Organize notes, assignments, quizzes, and exams to better understand course content

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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

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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

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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

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Practice Implementing Generative AI Techniques with Real-World Datasets

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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.

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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

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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.

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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

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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.

Browse courses on Generative AI Applications

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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.

Career center

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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NVIDIA-Certified Associate - Generative AI LLMs (NCA-GENL)

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