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

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Manifold AI Learning ®

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1. Introduction

1. Welcome to the Course.mp4

03:09

2. What makes this course Unique.mp4

05:42

2. Machine Learning Fundamentals

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

3. Fundamentals of Deep Learning

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

4. Essentials of NLP

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

5. Large Language Models

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

6. Prompt Engineering for the NCA-GENL Exam

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

7. Data Analysis and Visualization

1. Data Visualization & Analysis of LLMs.mp4

05:21

2. EDA for LLMs.mp4

05:28

8. Experimentation

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

9. LLM integration & Deployment

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

10. Trustworthy AI

1. Building Trustworthy AI & NVIDIA Tools.mp4

07:05

2. Trustworthy AI - Exam Guide.mp4

01:51:47

11. Important - Exam Scheduling - Exam Registration Guide

1. Exam Tips & Instructions - watch this completely.mp4

27:33

Description

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

Who is this 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.

What You Need to Know?

Basic programming experience (Python recommended)

Fundamental understanding of machine learning concepts

Access to a computer with internet connectivity for online learning

More details

Description
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.
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:
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.
Who this course is 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.

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Manifold AI Learning ® is an online Academy with the goal to empower the students with the knowledge and skills that can be directly applied to solving the Real world problems in Data Science, Machine Learning and Artificial intelligence.Checkout our instructor profile for the complete list of courses.All the best for your Learning.- Team ManifoldAILearning ®"Learn the Future"

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