Generative AI and ChatGPT Masterclass for Software Engineers from Udemy

You don't have to buy 7-8 courses to learn - ChatGPT, Open AI APIs, Generative AI, LLM key concepts, Github Copilot, Langchain, Amazon Q Developer, Amazon Q Business, Amazon Bedrock, Google Gemini, GCP Vertex AI and Open AI Sora Architecture .

Welcome to our Comprehensive Course which covers all of these topics and we keep adding new topics every month .

Key Features:

- Artificial Intelligence Fundamentals: Build a solid foundation in AI, exploring key concepts and practical applications of Deep Learning and Generative AI.

- Boosting Productivity with Effective Prompt Engineering: Discover the secrets of ChatGPT through mastering prompt engineering. Optimize prompts for maximum productivity and creativity. Elevate your career with impressive resumes and interview preparations using ChatGPT's capabilities.

- Exploring Alternatives and Diving into DALL-E: Delve into alternative models like Bard and Bing in chat-based AI. Compare DALL-E variants to understand their unique features.

- OpenAI Deep Dive: Navigate the OpenAI ecosystem confidently, exploring models, APIs, and integration with tools like Postman.

- Generative AI Internals: Dive deep into Generative AI internals, understanding its impact on industries.

- Jupyter Notebook and Practical Examples: Master Jupyter Lab for seamless interaction with OpenAI models through practical examples.

- Token Pricing, SQL Interaction, and Hands-on Programs: Understand token pricing and leverage generative models for natural language interactions with data.

- Advanced Generative AI: Explore advanced techniques like embeddings and fine-tuning for real-world scenarios.

- Generative AI in AWS - Understand tools available in AWS for Generative AI application

- Amazon Bedrock - Master Amazon Bedrock with Hans-on exercises.

- Github Copilot: Revolutionizing Code Development: Discover Github Copilot's revolutionary impact on code development, enhancing productivity and efficiency.

- Real-World Project Implementation: Apply knowledge to hands-on projects integrating various OpenAI models.

- Google Gemini models: Learn the basics and applications of Google Gemini, comparing it with ChatGPT4.

- LangChain: Learn building LLM applications using LCEL(LangChain Expression Language) framework, RAG (Retrieval augmented generation), & building AI agents for complex tasks.

- OpenAI Sora - Text to Video Model: Master OpenAI Sora's architecture and diffusion models.

- Generative AI Future: Address job displacement concerns and explore new opportunities in the ever-evolving landscape.

Join Us on this AI Adventure: Unlock Your Potential

Whether you're aiming to enhance your career, optimize productivity, or explore AI's frontiers, our course is your comprehensive guide. Shape the future of technology with confidence. Let's unlock your potential together.

What's inside

Learning objectives

Hands-on programs for open ai models : whisper, dall-e 3, and embeddings.
Advanced generative ai : embeddings, vector db, fine-tuning, zero-shot, one-shot and few shot learning and rag (retrieval-augmented generation).
Real-world project implementation : hands-on project integrating openai models ( chatgpt, whisper, embeddings). uses rag and embeddings are plotted in graph.
Artificial intelligence : basics of ai. key concepts of ai, ai/ml differences, deep learning. real life examples of ai, ml, deep learning and generative ai.
Generative ai internals : gpt (generative pre-trained transformer), gen ai timeline, democratization of ai
Chatgpt 4o : effective prompts to improve productivity. build career - create resume and prepare for interview. 1000+ prompts with practical examples
Open ai : models, apis, invoking apis using postman, playground, tokens, temperature, pricing, usage, billing. productivity versus product development.
Open ai evolution: tracing the evolution and milestones of openai and chatgpt tech stack - examining the technological stack behind chatgpt.
Chatgpt alternatives : exploring alternative models like google gemini and copilot in the context of chat-based ai. comparing dall-e 2, dall-e 3 & midjourney.
Jupyter notebook and jupyter lab : using jupyter lab for seamless interaction. examples of using chatgpt and other models within a notebook .
Prompt examples in notebook : practical examples of using prompts within jupyter notebooks. tips for effective prompt formulation.

Calculate token pricing - tiktoken : introduction to tiktoken. understanding token pricing and calculating costs for different interactions.
Talk to your data - english lang for sql: leveraging generative models for natural language interactions with data.
Generative ai future : will ai take away jobs - addressing the common concern of job displacement by ai and new opportunities - strategies for professionals
Google gemini : explore the power of google gemini and it's various models ultra, pro and nano
Google gemini: image + text prompts, code generation, testing and debugging platform with replit integration
Github copilot: revolutionizing code development: discover github copilot's revolutionary impact on code development, enhancing productivity and efficiency.
Langchain: learn building llm applications using lcel(langchain expression language) framework, rag (retrieval augmented generation), & building ai agents
Openai sora - text to video model: master openai sora's architecture and diffusion models.
Will ai take away jobs or create more opportunities
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Hands-on programs for open ai models : whisper, dall-e 3, and embeddings.
Advanced generative ai : embeddings, vector db, fine-tuning, zero-shot, one-shot and few shot learning and rag (retrieval-augmented generation).
Real-world project implementation : hands-on project integrating openai models ( chatgpt, whisper, embeddings). uses rag and embeddings are plotted in graph.
Artificial intelligence : basics of ai. key concepts of ai, ai/ml differences, deep learning. real life examples of ai, ml, deep learning and generative ai.
Generative ai internals : gpt (generative pre-trained transformer), gen ai timeline, democratization of ai
Chatgpt 4o : effective prompts to improve productivity. build career - create resume and prepare for interview. 1000+ prompts with practical examples
Open ai : models, apis, invoking apis using postman, playground, tokens, temperature, pricing, usage, billing. productivity versus product development.
Open ai evolution: tracing the evolution and milestones of openai and chatgpt tech stack - examining the technological stack behind chatgpt.
Chatgpt alternatives : exploring alternative models like google gemini and copilot in the context of chat-based ai. comparing dall-e 2, dall-e 3 & midjourney.
Jupyter notebook and jupyter lab : using jupyter lab for seamless interaction. examples of using chatgpt and other models within a notebook .
Prompt examples in notebook : practical examples of using prompts within jupyter notebooks. tips for effective prompt formulation.
Calculate token pricing - tiktoken : introduction to tiktoken. understanding token pricing and calculating costs for different interactions.
Talk to your data - english lang for sql: leveraging generative models for natural language interactions with data.
Generative ai future : will ai take away jobs - addressing the common concern of job displacement by ai and new opportunities - strategies for professionals
Google gemini : explore the power of google gemini and it's various models ultra, pro and nano
Google gemini: image + text prompts, code generation, testing and debugging platform with replit integration
Github copilot: revolutionizing code development: discover github copilot's revolutionary impact on code development, enhancing productivity and efficiency.
Langchain: learn building llm applications using lcel(langchain expression language) framework, rag (retrieval augmented generation), & building ai agents
Openai sora - text to video model: master openai sora's architecture and diffusion models.
Will ai take away jobs or create more opportunities
Show more
Show less

Syllabus

Github link - All Prompts, Entire Code base & Important Links

Get familiar with Artificial Intelligence(AI), Machine Learning (ML), Deep Learning and Generative AI. Understand difference between Traditional AI v/s Generative AI and AI vs ML.

Uncover the power of AI! Learn the essentials of Artificial Intelligence and explore how it's transforming industries. Discover self-driving cars, smart assistants, medical breakthroughs, and more in this beginner-friendly course.

Tired of the confusion around AI buzzwords? This course breaks down AI, Machine Learning, Deep Learning, and Generative AI with clear explanations and real-world examples.

AI and ML are often used interchangeably, but they're not the same! Demystify these technologies, understand their relationship, and learn how they are used in the real world.

Uncover how Deep Learning powers the technologies you use daily. Explore medical diagnosis, and personalized recommendations in this engaging course.

Understand Power of Generative AI and what makes it powerful !!

Get clear explanations of Traditional and Generative AI. Understand their strengths, limitations, and real-world applications.

You would have heard of ChatGPT, but do you know what is GPT? This course breaks down each term: Generative, Pre-Trained, and Transformer. Learn how GPT works, its capabilities, and exciting real-world applications in text generation, translation, and more.

Quiz to test your knowledge on AI & Generative AI Introduction

Trace the story of OpenAI, from its founding to its groundbreaking ChatGPT and other language models. Discover Microsoft's investment and the implications for the future of AI.

Dive into the technologies behind ChatGPT. Explore Transformer architectures, and the role of open-source software. Get insights into the impact of open-source on AI development.

Trace the evolution of Generative AI from early concepts to today's breakthroughs. Explore key milestones, the contributions of Google and AWS, and the future of AI-powered creation.

Enhance your Generative AI journey by Engaging, Evaluating and Evolving this course !

Understand what is DALL-E and compare both the versions available.

Understand Midjourney and why it's one of the best tools to generate image.

Microsoft Copilot powered by ChatGPT is enhancing user experience with lot of cool new features. Let's see those in detail.

Understand basics of LangChain and LangChain frame work with easy examples

Langchain First hands-on program using LangChain Expression Language (LCEL) - Understand how easy it is to perform LLM operations using LCEL

Implement RAG using LangChain, made easy with LCEL

LangChain agents are very powerful tool. Hands-on exercise to implement google search with help of agents

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers a wide range of topics, including ChatGPT, OpenAI APIs, Generative AI, LLM concepts, GitHub Copilot, Langchain, Amazon Q, Bedrock, Google Gemini, and OpenAI Sora, which provides a comprehensive overview of the field

Includes hands-on programs for OpenAI models like Whisper, DALL-E 3, and Embeddings, allowing learners to gain practical experience with these tools

Explores advanced techniques like embeddings, vector databases, fine-tuning, and RAG (Retrieval-augmented generation), which are essential for building sophisticated AI applications

Includes a real-world project that integrates OpenAI models, such as ChatGPT, Whisper, and Embeddings, providing learners with the opportunity to apply their knowledge in a practical setting

Teaches LangChain, which is used for building LLM applications using LCEL, RAG, and AI agents, which are valuable skills for developing complex AI systems

Addresses the concern of job displacement by AI and explores new opportunities for professionals, which is relevant in the rapidly evolving field of AI

Reviews summary

Comprehensive generative ai for engineers

According to learners, this course is a largely positive and comprehensive introduction to Generative AI and LLMs, specifically tailored for software engineers. Students praise its wide coverage of topics, from foundational AI concepts and prompt engineering to hands-on labs with OpenAI APIs, LangChain, and other tools. Many highlight the instructor's efforts to keep the content updated, addressing the rapidly evolving field. The practical demos and real-world project implementation are frequently mentioned as valuable assets. While some find the breadth means less depth in specific areas, the overall sentiment indicates it provides a strong practical foundation.

Detailed section on prompt engineering techniques.

"The section on prompt engineering is very detailed and practical."

"Learning different prompt techniques significantly boosted my productivity."

"The 1000+ prompts examples are a great resource."

"Level 6 on resume and interview prep with ChatGPT was a unique and helpful touch."

Explains core AI/ML/Gen AI concepts well.

"It lays a solid foundation for understanding Generative AI concepts."

"The explanations of LLMs, Embeddings, and RAG were very clear."

"Helped clarify the differences between AI, ML, Deep Learning, and Gen AI."

"Good overview of Transformer architecture and how GPT works."

Instructor actively updates course material.

"It's clear the instructor is keeping this course updated with the latest changes in the field."

"Adding modules on Sora and new models shows commitment to relevance."

"I value that they add new topics every month, it keeps it current."

"Seeing updates based on new AI developments is a major plus."

Strong focus on coding, APIs, and projects.

"The hands-on coding and projects are the strongest part of the course for me."

"Implementing the OpenAI APIs programmatically was incredibly useful."

"The real-world project integrating different models really tied things together."

"Using Jupyter Notebooks for the examples made it easy to follow along and practice."

Covers a wide range of Generative AI topics.

"This course provides an excellent, comprehensive overview of Generative AI."

"It covers so many relevant topics, from basic AI to LangChain and Bedrock. Very extensive."

"I appreciate how many different models and tools are introduced, it's very thorough."

"Learned about OpenAI, Gemini, LangChain, Bedrock, Sora - it's all here."

Some users faced minor setup or API issues.

"Ran into a few minor technical hiccups setting up the environment for some labs."

"Occasionally, API results didn't match the demo, likely due to external changes."

"While issues were usually solvable, a few parts required troubleshooting beyond the course."

Wide scope limits depth in certain areas.

"While comprehensive, some sections felt a bit rushed due to the sheer amount of topics."

"Could use more in-depth coverage on complex topics or optimization techniques."

"It's a great overview, but I'll need other resources for deep dives into specific models."

"The breadth is good, but don't expect expert-level detail on every single tool."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI and ChatGPT Masterclass for Software Engineers with these activities:

Review AI/ML Fundamentals

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Reinforce your understanding of AI and ML concepts to better grasp the advanced topics covered in the course.

Browse courses on Artificial Intelligence

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Review the differences between AI, ML, and DL.
Study the typical ML model building process.
Identify real-world applications of AI/ML.

Review 'Generative AI with Python and TensorFlow 2'

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Supplement your understanding of generative AI models with practical examples and implementation details.

View Putting Knowledge to Work on Amazon

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Read the chapters on GANs and VAEs.
Experiment with the provided code examples.
Compare the book's approach with the course material.

Experiment with Prompt Engineering

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Practice prompt engineering techniques to improve your ability to generate desired outputs from LLMs.

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Try different prompt engineering techniques.
Evaluate the outputs for quality and relevance.
Refine your prompts based on the results.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Blog Post on Generative AI Applications

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Solidify your understanding of generative AI by researching and writing about its various applications.

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Research different applications of generative AI.
Write a blog post summarizing your findings.
Include examples and real-world use cases.

Build a Chatbot with OpenAI APIs

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Apply your knowledge of OpenAI APIs to create a functional chatbot that interacts with users.

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Set up your OpenAI API credentials.
Design the chatbot's functionality.
Implement the chatbot using Python and the OpenAI API.
Test and refine the chatbot's performance.

Review 'LangChain in Motion'

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Supplement your understanding of LangChain with practical examples and implementation details.

View Putting Knowledge to Work on Amazon

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Read the chapters on LCEL and RAG.
Experiment with the provided code examples.
Compare the book's approach with the course material.

Contribute to a LangChain Project

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Deepen your understanding of LangChain by contributing to an open-source project.

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Find an open-source LangChain project on GitHub.
Identify an issue or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete Generative AI and ChatGPT Masterclass for Software Engineers will develop knowledge and skills that may be useful to these careers:

AI Engineer

An Artificial Intelligence engineer specializes in developing and implementing AI models and systems. This course provides a practical understanding of the various generative AI tools and frameworks, including an understanding of foundational models, embeddings, vector databases, and fine-tuning. The course explores the OpenAI ecosystem, including models, APIs, and integration with Postman, which helps an AI Engineer learn how to build applications. The course also teaches how to use Langchain and RAG, critical skills to be successful in this role. With detailed coverage of AI fundamentals as well as advanced topics, this course is highly relevant for an AI engineer.

See salaries and explore the career path for AI Engineer

Prompt Engineer

A prompt engineer specializes in crafting effective prompts to elicit desired outputs from AI models. This course is highly relevant for a prompt engineer, as it focuses on the art of prompt engineering for ChatGPT, and provides detailed coverage on how to optimize prompts for productivity and creativity. The course's prompt engineering section goes over 1000+ practical examples, including techniques, tips, and risks, which is perfect for a prompt engineer. With all of the ways that this course explores prompts, a prompt engineer will be well served by taking this course.

See salaries and explore the career path for Prompt Engineer

Machine Learning Engineer

A Machine Learning Engineer focuses on building and deploying machine learning models, often working with large datasets. This course helps a machine learning engineer understand how to use emerging generative AI tools and techniques effectively. The course covers practical examples of how to use models like DALL-E, and includes hands-on programs for OpenAI models like Whisper and Embeddings, which is essential for building intelligent systems. The course also dives deep into advanced topics like vector databases and fine-tuning, which are critical for machine learning engineers working with cutting-edge AI.

See salaries and explore the career path for Machine Learning Engineer

Software Developer

A software developer builds and maintains software applications. This course helps developers learn how to integrate generative artificial intelligence into their projects, use tools like GitHub Copilot to increase efficiency, and master prompt engineering for better control over AI outputs. The course's hands-on approach, including practical examples and projects using OpenAI models, combined with instruction in Langchain and RAG, is ideal for developers looking to implement AI in their work. The course emphasis on prompt engineering, also provides software developers with a critical skill for maximizing the value of generative models in development.

See salaries and explore the career path for Software Developer

Automation Engineer

An automation engineer designs and implements systems to automate processes. This course helps an automation engineer explore how generative AI can be used to automate tasks, enhance existing workflows, and build more intelligent automated systems. It covers AI concepts, including foundational models, prompt engineering, and using tools like GitHub Copilot, which is invaluable for streamlining code development. The training on Langchain and AI agents could also help an automation engineer build more advanced automated systems. The course enables them to integrate AI into automated processes, thereby increasing efficiency and productivity.

See salaries and explore the career path for Automation Engineer

AI Trainer

An AI trainer develops training materials, conducts training sessions, and leads learning efforts to teach others how to use AI tools and techniques. This course is beneficial for an AI trainer because it offers a comprehensive view of the generative AI landscape and covers many platforms, including OpenAI, AWS, and Google Gemini. Hands-on experience with prompt engineering and Jupyter Notebooks provides valuable training materials for an AI trainer to use and create. By understanding the capabilities and limitations of AI, an AI trainer can build effective training programs.

See salaries and explore the career path for AI Trainer

Technology Educator

A technology educator teaches students about various technologies. This course helps a technology educator grasp the basics of artificial intelligence, deep learning, and generative AI. The course's practical examples and hands-on exercises, including prompt engineering, can be directly used as learning materials for students. By exploring various AI platforms, including OpenAI, Google Gemini, and AWS, a technology educator can stay up to date with the latest trends and tools. The course's focus on practical use cases may also help a technology educator design engaging educational content.

See salaries and explore the career path for Technology Educator

Data Scientist

A data scientist analyzes and interprets complex data to derive insights. This course may be useful to data scientists by teaching them how to leverage generative AI for data analysis, model development, and reporting. The course includes information about using generative models for interactions with data, which could help a data scientist use natural language queries with data sets. The hands-on projects and example use cases could help a data scientist explore new ways to use AI for enhanced analysis and visualization.

See salaries and explore the career path for Data Scientist

AI Product Manager

An AI product manager is responsible for guiding the development and launch of AI-powered products. This course may be useful for a product manager by providing insights into the capabilities and limitations of generative AI tools. The course would help an AI product manager understand the technical aspects of AI, particularly in areas such as models, APIs, and integration. The course includes information on how AI might impact jobs and create new opportunities, which is vital for developing a business strategy for AI product development. This course provides an overview of the various AI tools in the marketplace.

See salaries and explore the career path for AI Product Manager

Cloud Solutions Engineer

A cloud solutions engineer designs, implements, and manages cloud-based solutions. This course may help a cloud solutions engineer by providing an understanding of the generative AI tools and services available on cloud platforms, such as AWS and Google Cloud. This course also teaches how to use various tools like Postman for API integrations, which will help a cloud solutions engineer design and integrate cloud services for generative AI applications. This course may be useful in helping a cloud solutions engineer utilize different cloud platforms and understand the various AI tools available.

See salaries and explore the career path for Cloud Solutions Engineer

Solutions Architect

A solutions architect designs and oversees the implementation of complex systems. This course may be useful for a solutions architect by providing a broad overview of how to integrate generative AI technologies into various software architectures. The course covers integrations with tools like Postman, Jupyter Notebook, and Langchain, which are all key for crafting a comprehensive solution architecture. The exploration of AWS and Google's AI offerings, as well as the study of foundational models and multi-modal AI, may help a solutions architect by exposing them to different options when architecting AI solutions.

See salaries and explore the career path for Solutions Architect

Technical Consultant

A technical consultant advises clients on the best technical solutions for their business problems. This course may help a technical consultant develop an understanding of the capabilities of generative AI. The consultant would be able to use this knowledge to recommend, design, and implement AI solutions tailored to specific client needs. The course provides a broad overview of various tools and platforms, including OpenAI, Google Gemini, AWS, and Langchain. Because of the course's focus on practical applications and real-world examples, a technical consultant can gain insights into the practical use cases of AI.

See salaries and explore the career path for Technical Consultant

Research Scientist

Research Scientists explore new scientific questions, and an AI Research scientist may focus on the theoretical aspects of artificial intelligence and machine learning. This course, while focusing on practical applications, may be helpful by giving an overview of current generative AI models, techniques, and platforms, such as LangChain, which are frequently mentioned in research. By introducing the practical aspects of prompt engineering, a research scientist may discover new avenues for exploration. The course's deep dive into the internals of generative AI could also help a research scientist understand the current state of the art.

See salaries and explore the career path for Research Scientist

Data Analyst

A Data Analyst collects and interprets data to identify trends and patterns. While this course is more focused on generative AI, it may be helpful by demonstrating the use of AI in data analysis, such as using natural language to query data. The course's coverage of how to use AI models for natural language interactions with data could help a data analyst by allowing them to pull more insights from large datasets. The section on using Jupyter Notebooks for data analysis could be directly relevant to the work of a data analyst. All of these aspects show how this course may be helpful for a data analyst.

See salaries and explore the career path for Data Analyst

Robotics Engineer

A robotics engineer designs and develops robots and automated systems. An exploration of AI concepts may be useful to a robotics engineer, particularly the use of large language models and multi-modal AI systems for advanced robotic applications. The course may help a robotics engineer integrate AI into their robotic systems by exposing them to tools such as GitHub Copilot and Langchain. Since prompt engineering is taught, a robotics engineer may better understand how to use natural language processing in their AI systems.

See salaries and explore the career path for Robotics Engineer

Generative AI and ChatGPT Masterclass for Software Engineers

What's inside

Learning objectives

Syllabus

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Reviews summary

Comprehensive generative ai for engineers

Activities

Career center

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