Agentic AI for Beginners: Build Business AI Agents Bootcamp

Explore agentic AI and build autonomous AI agents for real-world business tasks. Learn goal-driven decision making with LLMs, tools, memory, and planning to automate complex workflows and enterprise solutions.

Differentiate ai models from ai agents: models generate outputs from prompts, while agents plan, decide, and act using tools, APIs, and memory to complete tasks end to end.

Explore real-world AI agents such as chart gpt, auto gpt, and ai copilots; learn how goal-driven, context-aware agents perceive, reason, and act to boost business productivity with human oversight.

Explore how agentic ai transforms industries with perception, planning, tools and api integration, memory, and continuous learning across healthcare, finance, manufacturing, and it, plus architecture, autonomous execution, and governance.

Explore a structured roadmap for agentic AI, with hands-on tools overview and real-world projects. Learn Python, PyCharm, Git, GitHub, APIs, and deployment tools across the full project lifecycle.

Master the future essential AI tools to boost productivity and career growth by automating workflows, mastering generative AI tools, and applying real-world use cases across professions.

Discover how generative AI uses training data and architectures such as GANs, VAEs, and transformers to create text, images, and code, with applications, benefits, and challenges.

Explore deep learning basics, including neural networks, backpropagation, and gradient descent. Discover how DL handles unstructured data like images, audio, and text and its impact across healthcare, automotive, and chatbots.

Explore generative AI for programmers, leveraging adaptive learning, creativity, and problem solving to accelerate development, enhance debugging, improve code optimization, and enable collaborative, automated code generation via prompts.

Learn to set up a ChatGPT account by signing up with email or continue with Google, Microsoft, or Apple, then log in to access the ChatGPT dashboard.

Learn to use ChatGPT OpenAI as a generative AI tool, set up an account, craft prompts, and generate Python code like a calculator.

Learn to install Python from python.org, add python.exe to the system path, and verify installation with idle. Set up a new project with the free PyCharm community edition.

Learn to install the PyCharm community edition for Python development, including setup steps, free access, and features like code analysis, debugger, unit tester, and version control integration.

Learn to create your first Python project in PyCharm, set up a new virtual environment with a base interpreter, and create main.py while adjusting IDE settings.

Learn to write and run a first Python hello world program in PyCharm, create a Python project, and verify exit code zero.

Refresher on basic python for ai: learn variables, data types, conditionals, loops, and functions with real-world examples in PyCharm, using virtual environments and libraries like NumPy, pandas, Matplotlib, and scikit-learn.

install OpenAI and LangChain libraries in a PyCharm project using pip, upgrade pip, and manage api keys with dot env through step-by-step guidance.

Install numpy, pandas, and matplotlib with pip, verify the installations, and run a real-world sales data plot in PyCharm to visualize charts.

Harness generative ai to enhance software development, enabling code generation, debugging, testing, and automated documentation across the software development life cycle, with Copilot, Tabnine, Codex, and transformers.

Leverage generative AI for retail to deliver personalized marketing and tailored product recommendations, improve customer service with chatbots, and optimize inventory through demand prediction and visual search.

Learn to craft lesson plans with generative ai, using chat gpt prompts, to design algebra lessons, include objectives, examples, common errors, guided and independent practice.

Learn to use generative AI to perform grammar corrections by turning ungrammatical sentences into standard English using prompts and chart gpt.

Learn to create a healthy diet chart for weight loss (and weight gain) with a week-long, balanced meal plan, using ChartGPT guidance, plus exercise tips and sleep recommendations.

Learn translating languages with generative AI, including English to French and German; craft prompts and test outputs with ChatGPT and Google Translate, and translate books, text, and audio.

Learn to use ChatGPT to generate a product sale plan ppt, with customizable slide counts, including title, market analysis, sales strategy, milestones, and design tips, downloadable as a ready-to-run presentation.

Explore large language models trained on massive text data, and how GPT, Claude, and Gemini empower agentic AI with natural language understanding, text generation, and context awareness.

unpack tokens, prompts, and completions as the building blocks of llm-based agentic ai, detailing prompt engineering, tokenization, and parameter-driven outputs for real-world development.

Master prompt engineering basics to improve ai outputs by designing clear inputs, providing context, using examples, and iterating. Explore zero-shot, few-shot, instructions-based, and role-based prompting with practical examples.

Explore zero-shot and few-shot prompting in AI, comparing no-example prompts with example-guided prompts for accuracy, structured output, and cost, and learn when to use each.

Explore the limitations of large language models, including hallucinations, lack of true understanding, context and data cut-off, bias, security risks, cost, and the need for human verification and prompt engineering.

Analyze how agentic AI agents autonomously plan, act, and adapt using tools, APIs, and memory to achieve goals with proactive, goal-driven behavior.

Understand agentic ai components memory, tools, and planning that empower autonomous ai agents, including memory types and planning methods like chain-of-thought and task decomposition.

Explore reactive, autonomous, and multi-agent systems, showing how reactive agents use no memory or planning, autonomous agents pursue goals with memory and planning, and multi-agent systems enable collaboration or competition.

Explore the agent lifecycle—input, goal understanding, planning, action, observation, and iteration—and see how loop-based agents use memory and tools to improve tasks like trip planning and book comparisons.

Learn how design thinking guides AI agent design by empathizing with users, defining problems, ideating, prototyping, and testing to create user-centric, cost-efficient AI solutions aligned with business goals.