Become an NVIDIA Certified Generative AI Specialist (NCA-GENL Exam Prep)
What you'll learn
Machine Learning Fundamentals
Deep Learning Fundamentals
Generative AI and LLMs
NVIDIA GPU Acceleration
Prompt Engineering
NCA-GENL Exam Preparation
Requirements
Basic programming experience (Python recommended)
Fundamental understanding of machine learning concepts
Access to a computer with internet connectivity for online learning
Description
NVIDIA Generative AI LLMs (NCA-GENL) Exam Prep: Become a Certified Generative AI SpecialistPrepare 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.NVIDIA GPU Acceleration: Harness the power of NVIDIA GPUs for accelerated model training, inference, and deployment, ensuring optimal performance and efficiency in real-world applications.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
evelopers 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 conceptsAccess to a computer with internet connectivity for online learningEnroll 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.
Overview
Section 1: Introduction
Lecture 1 Welcome to the Course
Lecture 2 What makes this course Unique
Section 2: Machine Learning Fundamentals
Lecture 3 Introduction to Machine Learning Fundamentals
Lecture 4 Introduction to Machine Learning
Lecture 5 Types of Machine Learning
Lecture 6 Linear Regression & Evaluation Metrics for Regression
Lecture 7 Regularization and Assumptions of Linear Regression
Lecture 8 Logistic Regression
Lecture 9 Gradient Descent
Lecture 10 Logistic Regression Implementation and EDA
Lecture 11 Evaluation Metrics for Classification
Lecture 12 Decision Tree Algorithms
Lecture 13 Loss Functions of Decision Trees
Lecture 14 Decision Tree Algorithm Implementation
Lecture 15 Overfit Vs Underfit - Kfold Cross validation
Lecture 16 Hyperparameter Optimization Techniques
Lecture 17 KNN Algorithm
Lecture 18 SVM Algorithm
Lecture 19 Ensemble Learning - Voting Classifier
Lecture 20 Ensemble Learning - Bagging Classifier & Random Forest
Lecture 21 Ensemble Learning - Boosting Adabost and Gradient Boost
Lecture 22 Emsemble Learning XGBoost
Lecture 23 Clustering - Kmeans
Lecture 24 Clustering - Hierarchial Clustering
Lecture 25 Clustering - DBScan
Lecture 26 Time Series Analysis
Lecture 27 ARIMA Hands On
Section 3: Fundamentals of Deep Learning
Lecture 28 Deep Learning Fundaments - Introduction
Lecture 29 Introduction to Deep Learning
Lecture 30 Introduction to Tensorflow & Create first Neural Network
Lecture 31 Intuition of Deep Learning Training
Lecture 32 Activation Function
Lecture 33 Architecture of Neural Networks
Lecture 34 Deep Learning Model Training. - Epochs - Batch Size
Lecture 35 Hyperparameter Tuning in Deep Learning
Lecture 36 Vanshing & Exploding Gradients - Initializations, Regularizations
Lecture 37 Introduction to Convolutional Neural Networks
Lecture 38 Implementation of CNN on CatDog Dataset
Lecture 39 Transfer Learning for Computer Vision
Lecture 40 Feed Forward Neural Network Challenges
Lecture 41 RNN & Types of Architecture
Lecture 42 LSTM Architecture
Lecture 43 Attention Mechanism
Lecture 44 Transfer Learning for Natural Language Data
Section 4: Essentials of NLP
Lecture 45 Introduction to NLP Section
Lecture 46 Introduction to NLP and NLP Tasks
Lecture 47 Understanding NLP Pipeline
Lecture 48 Text Preprocessing Techniques - Tokenization
Lecture 49 Text Preprocessing - Pos Tagging, Stop words, Stemming & Lemmatization
Lecture 50 Feature Extraction - NLP
Lecture 51 One Hot Encoding Technique
Lecture 52 Bag of Words & Count Vectorizer
Lecture 53 TF IDF Score
Lecture 54 Word Embeddings
Lecture 55 CBoW and Skip gram - word embeddings
Section 5: Large Language Models
Lecture 56 Introduction to Large Language Models
Lecture 57 How Large Language Models (LLMs) are trained
Lecture 58 Capabilities of LLMs
Lecture 59 Challenges of LLMs
Lecture 60 Introduction to Transformers - Attention is all you need
Lecture 61 Positional Encodings
Lecture 62 Positional Encodings - Deep Dive
Lecture 63 Self Attention & Multi Head Attention
Lecture 64 Self Attention & Multi Head Attention - Deep Dive
Lecture 65 Understanding Masked Multi Head Attention
Lecture 66 Masked Multi Head Attention - Deep Dive
Lecture 67 Encoder Decoder Architecture
Lecture 68 Customization of LLMs - Prompt Engineering
Lecture 69 Customization of LLMs - Prompt Learning - Prompt Tuning & p-tuning
Lecture 70 Difference between Prompt Tuning and p-tuning
Lecture 71 PEFT - Parameter Efficient Fine Tuning
Lecture 72 Training data for LLMs
Lecture 73 Pillars of LLM Training Data: Quality, Diversity, and Ethics
Lecture 74 Data Cleaning for LLMs
Lecture 75 Biases in Large Language Models
Lecture 76 Loss Functions for LLMs
Section 6: Prompt Engineering for the NCA-GENL Exam
Lecture 77 What is Prompt Engineering ?
Lecture 78 Advanced Prompt Engineering
Lecture 79 Techniques for Effective Prompts
Lecture 80 Ethical Considerations in Prompt Design for Large Language Models
Lecture 81 NVIDIA's Tools and Frameworks for Prompt Engineering
Lecture 82 NVIDIA Ecosystem tools for LLM Model Training
Section 7: Data Analysis and Visualization
Lecture 83 Data Visualization & Analysis of LLMs
Lecture 84 EDA for LLMs
Section 8: Experimentation
Lecture 85 Experiment Design Principles for LLMs
Lecture 86 Techniques for Large Language Models Experimentation
Lecture 87 Data Management and Version Control for LLM experimentation
Lecture 88 NVIDIA Ecosystem tools for LLM Experimentation, Data Management and Version Cont
Section 9: LLM integration & Deployment
Lecture 89 LLM Integration and Deployment
Lecture 90 Deployment Considerations for Large Language Models
Lecture 91 Monitoring and Maintenance of Large Language Models
Lecture 92 Explainability and Interpretability of Large Language Models
Lecture 93 NVIDIA Ecosystem Tools for Deployment and Integration
Section 10: Trustworthy AI
Lecture 94 Building Trustworthy AI & NVIDIA Tools
Lecture 95 Trustworthy AI - Exam Guide
Section 11: Important - Exam Scheduling - Exam Registration Guide
Lecture 96 Exam Tips & Instructions - watch this completely
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.