Master Generative AI & LLMs: NVIDIA Pro Certification 2024
Focused View
17:54:15
1. Welcome to the Course.mp4
03:09
2. What makes this course Unique.mp4
05:42
1. Introduction to Machine Learning Fundamentals.mp4
05:45
2. Introduction to Machine Learning.mp4
16:52
3. Types of Machine Learning.mp4
03:59
4. Linear Regression & Evaluation Metrics for Regression.mp4
22:44
5. Regularization and Assumptions of Linear Regression.mp4
26:01
6. Logistic Regression.mp4
08:46
7. Gradient Descent.mp4
08:05
8. Logistic Regression Implementation and EDA.mp4
21:05
9. Evaluation Metrics for Classification.mp4
26:35
10. Decision Tree Algorithms.mp4
11:30
11. Loss Functions of Decision Trees.mp4
09:51
12. Decision Tree Algorithm Implementation.mp4
15:44
13. Overfit Vs Underfit - Kfold Cross validation.mp4
18:26
14. Hyperparameter Optimization Techniques.mp4
29:38
15. KNN Algorithm.mp4
09:31
16. SVM Algorithm.mp4
23:56
17. Ensemble Learning - Voting Classifier.mp4
14:32
18. Ensemble Learning - Bagging Classifier & Random Forest.mp4
17:04
19. Ensemble Learning - Boosting Adabost and Gradient Boost.mp4
17:50
20. Emsemble Learning XGBoost.mp4
09:16
21. Clustering - Kmeans.mp4
26:15
22. Clustering - Hierarchial Clustering.mp4
12:29
23. Clustering - DBScan.mp4
05:52
24. Time Series Analysis.mp4
12:33
25. ARIMA Hands On.mp4
11:42
1. Deep Learning Fundaments - Introduction.mp4
02:31
2. Introduction to Deep Learning.mp4
13:53
3. Introduction to Tensorflow & Create first Neural Network.mp4
19:22
4. Intuition of Deep Learning Training.mp4
15:04
5. Activation Function.mp4
08:40
6. Architecture of Neural Networks.mp4
05:40
7. Deep Learning Model Training. - Epochs - Batch Size.mp4
03:39
8. Hyperparameter Tuning in Deep Learning.mp4
08:29
9. Vanshing & Exploding Gradients - Initializations, Regularizations.mp4
07:13
10. Introduction to Convolutional Neural Networks.mp4
18:02
11. Implementation of CNN on CatDog Dataset.mp4
15:29
12. Transfer Learning for Computer Vision.mp4
18:15
13. Feed Forward Neural Network Challenges.mp4
23:17
14. RNN & Types of Architecture.mp4
20:53
15. LSTM Architecture.mp4
09:41
16. Attention Mechanism.mp4
13:40
17. Transfer Learning for Natural Language Data.mp4
12:08
1. Introduction to NLP Section.mp4
02:23
2. Introduction to NLP and NLP Tasks.mp4
08:44
3. Understanding NLP Pipeline.mp4
06:33
4. Text Preprocessing Techniques - Tokenization.mp4
09:45
5. Text Preprocessing - Pos Tagging, Stop words, Stemming & Lemmatization.mp4
06:54
6. Feature Extraction - NLP.mp4
02:17
7. One Hot Encoding Technique.mp4
03:58
8. Bag of Words & Count Vectorizer.mp4
07:40
9. TF IDF Score.mp4
07:45
10. Word Embeddings.mp4
08:30
11. CBoW and Skip gram - word embeddings.mp4
11:08
1. Introduction to Large Language Models.mp4
06:43
2. How Large Language Models (LLMs) are trained.mp4
09:55
3. Capabilities of LLMs.mp4
03:18
4. Challenges of LLMs.mp4
06:56
5. Introduction to Transformers - Attention is all you need.mp4
09:24
6. Positional Encodings.mp4
08:48
8. Self Attention & Multi Head Attention.mp4
05:21
9. Self Attention & Multi Head Attention - Deep Dive.mp4
09:21
10. Understanding Masked Multi Head Attention.mp4
02:38
11. Masked Multi Head Attention - Deep Dive.mp4
05:52
12. Encoder Decoder Architecture.mp4
06:35
13. Customization of LLMs - Prompt Engineering.mp4
11:15
14. Customization of LLMs - Prompt Learning - Prompt Tuning & p-tuning.mp4
10:39
15. Difference between Prompt Tuning and p-tuning.mp4
02:52
16. PEFT - Parameter Efficient Fine Tuning.mp4
06:54
17. Training data for LLMs.mp4
08:12
18. Pillars of LLM Training Data Quality, Diversity, and Ethics.mp4
09:18
19. Data Cleaning for LLMs.mp4
09:05
20. Biases in Large Language Models.mp4
07:20
21. Loss Functions for LLMs.mp4
06:05
1. What is Prompt Engineering .mp4
07:39
2. Advanced Prompt Engineering.mp4
02:40
3. Techniques for Effective Prompts.mp4
04:34
4. Ethical Considerations in Prompt Design for Large Language Models.mp4
06:02
5. NVIDIAs Tools and Frameworks for Prompt Engineering.mp4
05:07
6. NVIDIA Ecosystem tools for LLM Model Training.mp4
04:00
1. Data Visualization & Analysis of LLMs.mp4
05:21
2. EDA for LLMs.mp4
05:28
1. Experiment Design Principles for LLMs.mp4
06:33
2. Techniques for Large Language Models Experimentation.mp4
05:43
3. Data Management and Version Control for LLM experimentation.mp4
04:27
4. NVIDIA Ecosystem tools for LLM Experimentation, Data Management and Version Cont.mp4
05:41
1. LLM Integration and Deployment.mp4
08:09
2. Deployment Considerations for Large Language Models.mp4
04:28
3. Monitoring and Maintenance of Large Language Models.mp4
05:22
4. Explainability and Interpretability of Large Language Models.mp4
07:02
5. NVIDIA Ecosystem Tools for Deployment and Integration.mp4
06:38
1. Building Trustworthy AI & NVIDIA Tools.mp4
07:05
2. Trustworthy AI - Exam Guide.mp4
01:51:47
1. Exam Tips & Instructions - watch this completely.mp4
27:33
More details
Course Overview
This comprehensive course prepares you for the NVIDIA Certified Generative AI Specialist (NCA-GENL) exam, covering machine learning fundamentals to advanced LLM deployment with hands-on GPU acceleration techniques.
What You'll Learn
- Core machine learning algorithms and deep learning architectures
- Transformer models, attention mechanisms, and prompt engineering
- Ethical AI practices and NVIDIA tools for LLM deployment
Who This Is For
- Developers integrating generative AI into applications
- Data scientists leveraging LLMs for NLP tasks
- AI professionals pursuing NVIDIA certification
Key Benefits
- Hands-on experience with NVIDIA's GPU-accelerated tools
- Industry-recognized certification preparation
- End-to-end LLM training to deployment workflow
Curriculum Highlights
- Machine Learning & Deep Learning Foundations
- Transformer Architectures & LLM Customization
- Prompt Engineering & Ethical Deployment
Focused display
- language english
- Training sessions 95
- duration 17:54:15
- Release Date 2025/05/25
