Generative AI with Python

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LLMs, Vector DBs, RAG, Agentic Systems, and more
⏱️ Length: 10.0 total hours
⭐ 4.35/5 rating
πŸ‘₯ 3,376 students
πŸ”„ July 2025 update

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  • Course Overview

  • This course offers a practical deep dive into Generative AI, leveraging Python to build advanced, intelligent applications from the ground up.
  • Explore the modern Generative AI stack, encompassing Large Language Models (LLMs), efficient data retrieval via Vector Databases, and sophisticated agentic architectures.
  • The curriculum guides you through transforming theoretical AI concepts into deployable solutions, focusing on real-world utility and problem-solving.
  • Stay ahead with content updated for July 2025, ensuring you learn the most current techniques and best practices in the rapidly evolving Generative AI landscape.
  • Designed as a 10-hour intensive program, it balances foundational understanding with extensive hands-on coding exercises, fostering a learn-by-doing approach.
  • Understand the synergistic integration of various AI components to create robust systems capable of complex reasoning, information synthesis, and autonomous task execution.
  • Prepare to develop innovative AI solutions that intelligently interact with diverse data, generate contextually rich responses, and automate sophisticated workflows.

  • Requirements / Prerequisites

  • Intermediate Python Proficiency: Solid understanding of Python fundamentals, including data structures, control flow, functions, and object-oriented programming is essential for effective participation.
  • Basic Machine Learning Concepts: While not strictly an ML course, general familiarity with terms like models, datasets, and inference will provide helpful context.
  • Development Environment Setup: Access to a computer capable of running Python 3.9+ and installing libraries, with basic command-line proficiency.
  • No Prior Generative AI Experience: This course introduces core Generative AI concepts from scratch, assuming no prior exposure to LLMs, Vector DBs, or Agentic Systems.

  • Skills Covered / Tools Used

  • Advanced Python for AI: Master Python’s application in complex AI workflows, including asynchronous operations, API interactions, and library integration for generative tasks.
  • Effective Prompt Engineering: Learn to design and optimize prompts to elicit precise and high-quality responses from LLMs, covering various prompting strategies and best practices.
  • Text Embedding Generation: Acquire skills in transforming textual data into numerical vector embeddings, foundational for semantic search and contextual understanding.
  • Vector Database Interaction: Gain practical experience with leading Vector Databases, including data ingestion, indexing, and efficient similarity querying for large datasets.
  • Retrieval-Augmented Generation (RAG) Implementation: Develop proficiency in architecting and deploying RAG pipelines to enable LLMs to leverage external knowledge bases for accurate and current responses.
  • Building Agentic Systems: Construct autonomous AI agents capable of planning, tool use, and multi-step reasoning to accomplish complex tasks within dynamic environments.
  • AI Orchestration Frameworks: Practical application of popular frameworks like LangChain or LlamaIndex to chain together LLMs, vector stores, and custom logic into sophisticated AI applications.
  • Evaluation of AI System Performance: Learn methodologies to assess the effectiveness, accuracy, and reliability of your Generative AI applications and agentic workflows.

  • Benefits / Outcomes

  • Become a Generative AI Developer: Emerge with the practical skills and confidence to design, build, and deploy sophisticated Generative AI applications and intelligent agents.
  • Master Modern AI Architectures: Gain a deep understanding of the full Generative AI stack, from LLMs to Vector DBs and agentic frameworks, positioning you as an informed expert.
  • Enhance Problem-Solving Capabilities: Learn to apply cutting-edge AI techniques to solve complex business problems, innovate new products, and automate intricate processes.
  • Future-Proof Your Career: Acquire highly sought-after skills in Generative AI, positioning yourself at the forefront of AI innovation and opening doors to exciting career opportunities.

  • PROS

  • Highly Practical and Hands-On: Emphasizes building real-world applications, ensuring tangible coding and development experience rather than just theoretical knowledge.
  • Up-to-Date Content (July 2025): Learners benefit from the most current tools, techniques, and best practices in the rapidly evolving Generative AI landscape.
  • Comprehensive Skill Set: Covers the entire modern Generative AI ecosystem, from foundational LLMs and data management to advanced RAG and autonomous agents.

  • CONS

  • Time Commitment for Depth: While 10 hours provides a strong foundation, mastering the advanced concepts and tools for complex real-world applications will require significant practice beyond the course duration.
Learning Tracks: English,Development,Data Science
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