Foundation Course on AI, Machine Learning and Generative AI

Master AI, ML, and LLM to Build Intelligent Applications and Accelerate Your Career From Foundations to Advanced Agents
What you'll learn
Understand core concepts of Artificial Intelligence and Machine Learning including supervised and unsupervised learning.
Build and evaluate machine learning models using Python and libraries like scikit-learn, TensorFlow, and PyTorch.
Apply AI concepts to real-world problems through guided hands-on projects and hackathon challenges.
Master foundational tools and techniques to transition into an AI/ML career confidently, even with an IT background.
Requirements
Basic knowledge of programming concepts is helpful but not mandatory. Curiosity, commitment, and a willingness to learn are all you need - everything else will be taught from scratch.
Description
What you'll learn (Key takeaways)evelop a strong understanding of fundamental AI/ML principles and the role of LLMs within the AI ecosystem.Explore different types of machine learning, including Supervised, Unsupervised, and Reinforcement Learning, with real-world examples.Understand core machine learning concepts like model lifecycle, overfitting, loss functions, and evaluation metrics.Gain proficiency in Natural Language Processing (NLP) essentials and text representation techniques.Master advanced prompt engineering techniques, including Zero-shot, Few-shot, Role, Persona, and Chain-of-Thought prompting.Learn to integrate LLMs into applications using orchestration frameworks like LangChain.Understand and utilize vector databases for enhanced retrieval in LLM applications.Implement Retrieval-Augmented Generation (RAG) architectures to improve accuracy and reduce hallucinations in enterprise applications.Discover how LLMs can interact with external systems through tool and function calling.Build LangChain agents that leverage external tools to automate workflows.Delve into advanced agent design patterns and implement robust memory mechanisms for intelligent agents.Understand deployment strategies for LLM applications on cloud platforms, including containerization and serverless options.Learn key metrics and best practices for monitoring LLM performance and cost.Gain knowledge of ethical AI, responsible LLM development, and security best practices for LLMs.Explore advanced generative AI for code generation, data analysis, visualization, and creative content.Understand advanced RAG architectures like multi-hop and self-correcting RAG.Design complex, autonomous multi-agent systems and implement human-in-the-loop strategies.Develop practical skills by building real-world applications and managing your GitHub and LinkedIn profiles.Gain hands-on experience with tools like Gemini, OpenRouter, DeepSeek, Kimmy, Minimax, Genpark, and Qwen.Learn to generate applications quickly using AI tools and understand the evolving role of developers.Who is this course for?Developers and technical professionals looking to build intelligent applications using AI/ML and LLMs.Individuals interested in understanding the fundamentals and advanced concepts of AI, Machine Learning, and Deep Learning.Content creators and freelancers who want to leverage AI tools for their work.Anyone with a business idea looking to build applications without extensive technical background (vibe coding).Those seeking to enhance their career prospects in the rapidly evolving AI and ML landscape.Participants are recommended to have basic Python programming skills and familiarity with the command line interface.
Who this course is for
This course is ideal for IT professionals, fresh graduates, and tech enthusiasts who are curious about Artificial Intelligence and Machine Learning. Whether you're switching careers, upskilling, or preparing for your first AI job, this course gives you a practical, beginner-friendly introduction without needing prior experience in AI/ML.
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