Description
Complete Gen AI: Basic to Agent AI, RAG, Bedrock, Vertex AI. This comprehensive, hands-on course teaches the full potential of Generative AI, from the basics of NLP to implementing intelligent agents and RAG architectures on platforms like AWS Bedrock and Google Vertex. Designed for students, developers, and enthusiasts, the course is designed to help even beginners get started by teaching them the basics of Python and the basics of Generative AI. It then moves on to advanced topics like building applications with LangChain, working with LangSmith and LangGraph tools, and exploring intelligent agents like Crew AI and AutoGen that can transform areas like customer service and automation. The Advanced RAG Techniques section teaches methods like Vector RAG and Graph RAG using the Neo4j database, as well as the concept of Self-Reflective RAG as the next frontier in AI reasoning. By providing exams, coding challenges, and hands-on projects, this course ensures theoretical understanding and practical experience in key areas of productive AI, preparing participants to build AI solutions from the ground up.
What you will learn
- Mastering the basics of NLP:
- Tokenization, Embedding, POS Tagging, TF-IDF, Chunking and more.
- Understanding the principles of generative artificial intelligence:
- Explore key concepts such as Autoencoders, VAEs, GANs, and Transformer models.
- Mastery of Prompt Engineering:
- Learn techniques for designing effective prompts for models like ChatGPT, including Zero-shot, One-shot, and Few-shot Prompting.
- Working with industry-leading tools:
- Explore advanced generative AI platforms such as ChatGPT, Google Gemini, and Microsoft CoPilot for real-world applications.
- Setting up the environment for practical applications of generative AI:
- Implementing RAG using Python, VS Code, and LangChain.
- Working with LangChain and LangChain Ecosystem Libraries (LCEL):
- Building real-world productive AI applications and exploring the LangChain ecosystem.
- Agent AI Development:
- Understand and implement agents like Crew AI and AutoGen to automate complex tasks.
- Implementing Vector RAG and Graph RAG:
- Using Neo4j for advanced retrieval and data augmentation techniques.
- Learning Self-Reflective RAG Techniques:
- Understanding how AI reasons and reflects on its processes.
- Practical Python skills for productive AI:
- Starting from the basics and progressing to advanced AI development with Python and libraries like NLTK.
- Building AI solutions from scratch:
- Gain a thorough knowledge of Generative Artificial Intelligence, from basics to advanced implementations with LangChain and LCEL.
- Generative AI with AWS Bedrock
- Generative AI with Google Cloud Vertex AI
- Implementing a practical application with AWS Bedrock BOTO3
- Implementing a practical application of Google Cloud Vertex AI
This course is suitable for people who:
- Data Scientists
- Machine Learning Engineers
- Artificial Intelligence and NLP enthusiasts
- Software developers and engineers
- Researchers and academics
- Product managers and technical leaders
- Students and trainees
- Artificial Intelligence Experts and Consultants
- Quality Engineers
Course details
- Publisher: Udemy
- Instructor: Soumen Kumar Mondal
- Training level: Beginner to advanced
- Training duration: 7 hours and 53 minutes
- Number of lessons: 57
Course syllabus in 2025/8
Complete Gen AI: Basic to Agent AI RAG Bedrock Vertex AI Course Prerequisites
- Basic understanding of Python but don’t worry the course will cover fundamentals of Python.
Course images
Sample course video
Installation Guide
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Subtitles: None
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
4.3 GB
