English | Size: 4.6 GB
Genre: eLearning
Generative AI Made Simple: Build Intelligent, Innovative Applications—No AI/ML Experience Needed
What you’ll learn
Learners will master the core concepts and principles behind Generative AI, how LLMs work, and how they can be applied in real-world use cases.
Develop practical skills to design, architect, and implement smart applications leveraging a wide range of LLMs from both open and closed sources.
Learn how to effectively use popular tools including LangChain, HuggingFace, StreamLit, Ollama, and more
Understand and implement best practices when it comes to efficiency, scalability, and responsible use of generative AI in production environments.
Identify and navigate challenges specific to generative AI applications.
Prepare for intermediate-level technical interviews for roles in Generative AI.
Learn how In-context learning, RAG and Fine-tuning works under the covers
Are you interested in learning generative AI, but feel intimidated by the complexities of AI and ML?
If your answer is YES, then this course is for you!
I structured this course based on my own journey learning generative AI technology. Having faced the challenges firsthand, I’ve designed it to make the learning process easier and more accessible. This course is tailored specifically for those without an AI or ML background, helping you quickly get up to speed with generative AI.
Designed specifically for IT professionals, developers, and architects with no prior AI/ML background, this course will empower you to build intelligent, innovative applications using Large Language Models (LLM). You’ll gain practical, hands-on experience in applying cutting-edge generative AI technologies without the steep learning curve of mastering complex algorithms or mathematical theories.
Here is an overview of course structure & coverage:
Generative AI Foundations: Dive into the core concepts of Large Language Models (LLM), and learn how to work with powerful models like Google Gemini, Anthropic Claude, OpenAI GPT, and multiple open-source/Hugging Face LLMs.
Building Generative AI Applications: Discover practical techniques for creating generative AI applications, including prompting techniques, inference control, in-context learning, RAG patterns (naive and advanced), agentic RAG, vector databases & much more.
Latest Tools and Frameworks: Gain practical experience with cutting-edge tools like LangChain, Streamlit, Hugging Face, and popular vector databases like Pinecone and ChromaDB.
Try out multiple LLM: Course doesn’t depend on a single LLM for hands-on exercises, rather learners are encouraged to use multiple models for exercises so that they learn the nuances of their behavior.
Learning Reinforcement: After each set of conceptual lessons, students are given exercises, projects, and quizzes to solidify their understanding and reinforce the material covered in previous lessons.
Harnessing the Power of Hugging Face: Master the Hugging Face platform, including its tools, libraries, and community resources, to effectively utilize pre-trained models and build custom applications.
Advanced Techniques: Delve into advanced topics like embeddings, search algorithms, model architecture, and fine-tunings to enhance your AI capabilities.
Real-World Projects: Apply your knowledge through hands-on projects, such as building a movie recommendation engine and a creative writing workbench.
Course Features
18+ Hours of Video Content
Hands-On Projects and Coding Exercises
Real-World Examples
Quizzes for Learning Reinforcement
GitHub Repository with Solutions
Web-Based Course Guide
By the end of this course, you’ll be well-equipped to leverage Generative AI for a wide range of applications, from natural language processing to content generation and beyond.
Who Is This Course For?
This course is perfect for:
IT professionals, application developers, and architects looking to integrate generative AI into their applications.
Students or professional preparing for interviews for the roles related to generative AI
Those with no prior experience in AI/ML who want to stay competitive in today’s rapidly evolving tech landscape.
Anyone interested in learning how to build intelligent systems that solve real-world business problems using AI.
Why Choose This Course?
Raj structured this course based on his own experience in learning Generative AI technology. He applied his first hand knowledge of challenges faced in learning generative to create a structured course aimed at making it simple for anyone without AI/ML background to be able to get up to speed with generative ai fast.
No AI/ML Background Needed: This course is designed for non-experts and beginners in AI/ML.
Hands-On Learning: Engage in practical, real-world projects and coding exercises that bring AI concepts to life.
Expert Guidance: Learn from Rajeev Sakhuja, a seasoned IT consultant with over 20 years of industry experience.
Comprehensive Curriculum: Over 18 hours of video lessons, quizzes, and exercises, plus a web-based course guide to support you throughout your learning journey.
Latest Tools and Frameworks: Gain practical experience with cutting-edge tools like LangChain, Streamlit, Hugging Face, and popular vector databases like Pinecone , FAISS, and ChromaDB
Who should NOT take this course?
Folks looking for deep dive into the internals of generative AI models
Looking to gain understanding of mathematics behind the models
IT professionals interested in DataSciences role
Who this course is for:
Solution/Application Architects: Professionals looking to design cutting-edge applications that leverage LLMs to create more interactive, efficient, and intelligent solutions.
Application Developers: Interested in acquiring hands-on experience with tools, techniques, and frameworks to build AI-driven applications that can understand, generate, and respond intelligently to user inputs.
IT Professionals: Individuals aiming to transition into the growing field of Generative AI by gaining foundational knowledge and practical skills in AI-powered application development.
Data Scientists: Data professionals who want to expand their expertise by learning how to apply generative AI models to create sophisticated applications, beyond traditional data analysis and machine learning.
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