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

سرفصل های دوره

Become an NVIDIA Certified Generative AI Specialist (NCA-GENL Exam Prep)


1. Introduction
  • 1. Welcome to the Course
  • 2. What makes this course Unique

  • 2. Machine Learning Fundamentals
  • 1. Introduction to Machine Learning Fundamentals
  • 2. Introduction to Machine Learning
  • 3. Types of Machine Learning
  • 4. Linear Regression & Evaluation Metrics for Regression
  • 5. Regularization and Assumptions of Linear Regression
  • 6. Logistic Regression
  • 7. Gradient Descent
  • 8. Logistic Regression Implementation and EDA
  • 9. Evaluation Metrics for Classification
  • 10. Decision Tree Algorithms
  • 11. Loss Functions of Decision Trees
  • 12. Decision Tree Algorithm Implementation
  • 13. Overfit Vs Underfit - Kfold Cross validation
  • 14. Hyperparameter Optimization Techniques
  • 15. KNN Algorithm
  • 16. SVM Algorithm
  • 17. Ensemble Learning - Voting Classifier
  • 18. Ensemble Learning - Bagging Classifier & Random Forest
  • 19. Ensemble Learning - Boosting Adabost and Gradient Boost
  • 20. Emsemble Learning XGBoost
  • 21. Clustering - Kmeans
  • 22. Clustering - Hierarchial Clustering
  • 23. Clustering - DBScan
  • 24. Time Series Analysis
  • 25. ARIMA Hands On

  • 3. Fundamentals of Deep Learning
  • 1. Deep Learning Fundaments - Introduction
  • 2. Introduction to Deep Learning
  • 3. Introduction to Tensorflow & Create first Neural Network
  • 4. Intuition of Deep Learning Training
  • 5. Activation Function
  • 6. Architecture of Neural Networks
  • 7. Deep Learning Model Training. - Epochs - Batch Size
  • 8. Hyperparameter Tuning in Deep Learning
  • 9. Vanshing & Exploding Gradients - Initializations, Regularizations
  • 10. Introduction to Convolutional Neural Networks
  • 11. Implementation of CNN on CatDog Dataset
  • 12. Transfer Learning for Computer Vision
  • 13. Feed Forward Neural Network Challenges
  • 14. RNN & Types of Architecture
  • 15. LSTM Architecture
  • 16. Attention Mechanism
  • 17. Transfer Learning for Natural Language Data

  • 4. Essentials of NLP
  • 1. Introduction to NLP Section
  • 2. Introduction to NLP and NLP Tasks
  • 3. Understanding NLP Pipeline
  • 4. Text Preprocessing Techniques - Tokenization
  • 5. Text Preprocessing - Pos Tagging, Stop words, Stemming & Lemmatization
  • 6. Feature Extraction - NLP
  • 7. One Hot Encoding Technique
  • 8. Bag of Words & Count Vectorizer
  • 9. TF IDF Score
  • 10. Word Embeddings
  • 11. CBoW and Skip gram - word embeddings

  • 5. Large Language Models
  • 1. Introduction to Large Language Models
  • 2. How Large Language Models (LLMs) are trained
  • 3. Capabilities of LLMs
  • 4. Challenges of LLMs
  • 5. Introduction to Transformers - Attention is all you need
  • 6. Positional Encodings
  • 8. Self Attention & Multi Head Attention
  • 9. Self Attention & Multi Head Attention - Deep Dive
  • 10. Understanding Masked Multi Head Attention
  • 11. Masked Multi Head Attention - Deep Dive
  • 12. Encoder Decoder Architecture
  • 13. Customization of LLMs - Prompt Engineering
  • 14. Customization of LLMs - Prompt Learning - Prompt Tuning & p-tuning
  • 15. Difference between Prompt Tuning and p-tuning
  • 16. PEFT - Parameter Efficient Fine Tuning
  • 17. Training data for LLMs
  • 18. Pillars of LLM Training Data Quality, Diversity, and Ethics
  • 19. Data Cleaning for LLMs
  • 20. Biases in Large Language Models
  • 21. Loss Functions for LLMs

  • 6. Prompt Engineering for the NCA-GENL Exam
  • 1. What is Prompt Engineering
  • 2. Advanced Prompt Engineering
  • 3. Techniques for Effective Prompts
  • 4. Ethical Considerations in Prompt Design for Large Language Models
  • 5. NVIDIAs Tools and Frameworks for Prompt Engineering
  • 6. NVIDIA Ecosystem tools for LLM Model Training

  • 7. Data Analysis and Visualization
  • 1. Data Visualization & Analysis of LLMs
  • 2. EDA for LLMs

  • 8. Experimentation
  • 1. Experiment Design Principles for LLMs
  • 2. Techniques for Large Language Models Experimentation
  • 3. Data Management and Version Control for LLM experimentation
  • 4. NVIDIA Ecosystem tools for LLM Experimentation, Data Management and Version Cont

  • 9. LLM integration & Deployment
  • 1. LLM Integration and Deployment
  • 2. Deployment Considerations for Large Language Models
  • 3. Monitoring and Maintenance of Large Language Models
  • 4. Explainability and Interpretability of Large Language Models
  • 5. NVIDIA Ecosystem Tools for Deployment and Integration

  • 10. Trustworthy AI
  • 1. Building Trustworthy AI & NVIDIA Tools
  • 2. Trustworthy AI - Exam Guide

  • 11. Important - Exam Scheduling - Exam Registration Guide
  • 1. Exam Tips & Instructions - watch this completely
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