Learn Generative AI online with hands-on work in Python, Pandas, NumPy, OpenAI / Gemini APIs, Flask, FastAPI, VS Code, and Google Colab. Build chatbots, content generators, image tools, and AI-powered apps with a syllabus that covers LLMs, prompt engineering, APIs, deployment, and real projects.
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Learning the tools is only one part of the plan. The other part is showing employers that you can build, explain, and deploy AI features that solve a real use case, which is why Inventateq keeps placement support tied to the work you complete in class. For Generative AI roles, that usually means a clearer resume, sharper project stories, and interview practice around APIs, LLMs, and prompt design.
Support is built around your course timeline. As you complete modules, the team helps you shape project work into a resume, then uses mock interviews and role guidance to prepare you for titles such as Generative AI Engineer, AI Application Developer, and Prompt Engineer.
Generative AI roles are opening up in product teams, software companies, digital agencies, automation teams, and startups that want AI features built into their apps. Pay usually rises as you move from prompt work and API integration into full application development and deployment ownership.
Generative AI Average Salary by Experience
Generative AI roles are opening up in product teams, software companies, digital agencies, automation teams, and startups that want AI features built into their apps. Pay usually rises as you move from prompt work and API integration into full application development and deployment ownership.
Generative AI Average Salary by Experience
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Inventateq has supported learners across multiple technology tracks with a classroom and online format that stays practical from the first module to the last project. The teaching style is direct, the pace is structured, and the support is built to help learners in the online Generative AI course keep moving without getting lost in theory.
We stand apart through our commitment to:
Attend live, instructor-led classes from anywhere with the same hands-on structure as our classroom batches. Follow along step-by-step, get real-time doubt support, and revisit recordings whenever you need to.
Those starting a tech career and needing a practical entry into AI app building.
Developers who want to add LLMs and generative features to existing applications.
Anyone who knows basic Python and wants to move into AI-focused work.
People who want to design better prompts for text and image generation.
Professionals building chat, content, or support workflows with AI.
Learners from non-AI roles who want a structured online path into generative AI.
Module-based plan: The course runs through 11 modules, starting with core ideas and ending with live projects.
Live online access: Attend from anywhere and follow the same practical class flow as the classroom track.
Project time built in: The schedule includes real build work, not just lecture time.
Mentor-led pacing: Each stage is taught in sequence so Python, LLMs, APIs, and deployment stay connected.
4.7 Star Rating from 1,432+ Google Reviews
Rated 4.9/5 by AI Students
Generative AI is moving from experiment to everyday software. Teams now expect chat, content, search, and media features to be built into products, and that creates demand for people who can work with LLMs, prompts, APIs, and deployment basics.
By the end of the course, learners move beyond theory and start handling model-driven tasks with confidence. They can explain the pieces, build the app flow, and show projects that make sense in interviews.
You will be able to describe what Generative AI is, how it differs from ML and deep learning, and where it fits in real products.
You will design prompts for text and image tasks using zero-shot, one-shot, few-shot, and role-based methods.
You will create conversational apps that use LLMs for support, Q&A, and assistant-style workflows.
You will call OpenAI and Gemini APIs, handle authentication, and integrate model output into software.
You will make tools for summaries, blogs, emails, image generation, and voice or video concepts.
You will turn the course projects into clear examples you can talk through for Generative AI Engineer and AI Application Developer roles.
The course starts with fundamentals, so beginners can follow it if they are prepared to learn Python basics and work through the modules in order. The later topics move into LLMs, prompts, APIs, and project builds, which gives a clear path from simple concepts to usable work.
Yes. The syllabus includes real-time projects such as a customer support chatbot, resume generator, blog content generator, image generator application, and an AI-powered Q&A system. Those projects are important because they show how the tools connect to real outcomes.
Placement support is part of the course flow, not an afterthought. Learners get help with resumes, mock interviews, project presentation, and role guidance tied to the skills they build during training.
Yes, this page is for the online format, so you can attend live classes from anywhere. The sessions are built around screen sharing, coding walkthroughs, and practical exercises rather than passive recordings.
The curriculum is organized into 11 modules, so learners can expect a structured run from basics through deployment and projects. The exact pace depends on the batch schedule, but the flow is designed to move in sequence rather than rush through the material.
No prior API or cloud experience is required. Those topics are introduced in the foundations and API modules, so you learn them alongside the rest of the course instead of being expected to know them before starting.
That is exactly what the later modules are for. The course covers chatbot applications, content generation, summarization, image generation, and AI app development so you can leave with build experience, not just notes.
Inventateq offers classroom training across multiple locations. Explore the branch nearest to you and check available batch timings.
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