<h2>About this course</h2>
<p>This 2-hour course from NVIDIA's <a href="/topics/deep-learning">Deep Learning</a> Institute demystifies <a href="/topics/generative-ai">generative AI</a> — one of today's most transformative technologies. You'll explore how generative models work, see real-world applications across industries, and understand the societal questions they raise. NVIDIA is a leader in AI hardware and education, so you're learning from those shaping the field.</p>

<h2>What you'll learn</h2>
<ul>
  <li>The core concepts behind generative AI: <a href="/topics/transformers">transformers</a>, <a href="/topics/neural-networks">neural networks</a>, and how models learn from data</li>
  <li>Practical applications of generative AI in content creation, coding, design, and customer service</li>
  <li>How <a href="/topics/llm">large language models</a> (LLMs) like ChatGPT work under the hood</li>
  <li>The difference between various generative AI tools and when to use each one</li>
  <li>Key ethical and societal implications: bias, copyright, job displacement, and responsible AI use</li>
  <li>How to evaluate generative AI outputs critically and spot limitations</li>
  <li>Emerging trends and opportunities in the generative AI landscape</li>
</ul>

<h2>Who this is for</h2>
<p>Whether you're curious about AI, exploring a career shift, or want to stay current as your industry evolves, this course meets you where you are. You don't need coding or math background — just genuine curiosity about how the AI tools shaping work and creativity actually function.</p>
<ul>
  <li><strong><a href="/for/students">Students</a></strong> — Build AI literacy before graduation to stand out in campus placements at Indian tech companies and startups</li>
  <li><strong>Career changers</strong> — Understand AI's potential and limitations before exploring roles in AI-adjacent fields like product, operations, or strategy</li>
  <li><strong><a href="/for/working-professionals">Working professionals</a></strong> — Learn how generative AI affects your industry and how to integrate these tools into your workflow responsibly</li>
</ul>

<h2>Prerequisites</h2>
<p>None — this course is beginner-friendly. No coding, math, or prior AI knowledge required. Just bring an open mind.</p>

<h2>Why this matters for Indian learners</h2>
<p>India's tech sector is rapidly adopting generative AI. Companies like TCS, Infosys, HCL, and smaller startups are integrating AI into workflows and hiring talent that understands both the opportunities and risks. This course gives you a common language to discuss AI in interviews and on the job.</p>
<p>As India moves toward becoming a global AI hub, early literacy in generative AI is a real career edge. Whether you aim for roles in AI consulting, software development, content creation, or management, understanding generative AI — not just using it — will set you apart from peers.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. NVIDIA offers this through their Deep Learning Institute as part of their commitment to AI education. No hidden fees, no paid upgrade required to complete it.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed for 2 hours of focused learning. You can complete it in one sitting or break it into two one-hour sessions across a week — whatever fits your schedule.</p>
<h3>Will I get a certificate?</h3>
<p>Yes. Upon completion, you'll receive a certificate from NVIDIA's Deep Learning Institute, which you can add to your LinkedIn or resume to demonstrate your foundational knowledge of generative AI.</p>

GPU-accelerated AI — deep learning institute (DLI).

NVIDIA

NVIDIA DLI's free 2-hour intro to generative AI — concepts, applications, and societal implications.

Generative AI Explained

<h2>About this course</h2>
<p>This hands-on NVIDIA DLI course teaches you how to build intelligent agents using <a href="/topics/llm">Large Language Models</a> (LLMs) enhanced with Retrieval-Augmented Generation (<a href="/topics/rag">RAG</a>). You'll learn to create systems that combine the reasoning power of LLMs with real-time access to external data—a critical skill for building AI applications that actually work in the real world. NVIDIA's deep expertise in AI infrastructure makes this an authoritative guide to modern LLM application development.</p>

<h2>What you'll learn</h2>
<ul>
<li>How to design and implement RAG architecture that connects LLMs to knowledge bases and databases</li>
<li>Vector embeddings fundamentals—converting text and data into forms LLMs can understand and retrieve efficiently</li>
<li>Building and managing vector stores to organize and search your data at scale</li>
<li>Using <a href="/topics/langchain">LangChain</a> to orchestrate LLM workflows, chains, and agent behavior</li>
<li>Practical techniques to reduce hallucinations and make LLM responses grounded in actual data</li>
<li>How to structure multi-step reasoning tasks where agents can plan, act, and retrieve information dynamically</li>
<li>Real-world deployment patterns for RAG systems in production environments</li>
</ul>

<h2>Who this is for</h2>
<p>If you're building with LLMs or want to go beyond simple chatbots, this course is for you. You'll benefit most if you already understand Python and have basic familiarity with how LLMs work—but the course doesn't assume you're an expert.</p>
<ul>
<li><strong>AI engineers and ML practitioners</strong> — level up from basic LLM usage to production-grade RAG systems that actually solve real problems</li>
<li><strong>Full-stack <a href="/for/developers">developers</a></strong> — add intelligent search and reasoning to your applications with proven frameworks and patterns</li>
</ul>

<h2>Prerequisites</h2>
<p>You'll need working knowledge of Python, basic understanding of how Large Language Models work, and comfort with APIs. Familiarity with JSON and REST concepts is helpful. Prior experience with machine learning is not required.</p>

<h2>Why this matters for Indian learners</h2>
<p>RAG agents are transforming how Indian tech companies handle customer support, content discovery, and enterprise search. Whether you're building for a startup, a fintech platform, or an e-commerce business, RAG skills directly translate to roles in AI engineering and AI product development—fields where demand far exceeds supply in India's job market. Companies across Bangalore, Delhi, and Mumbai are actively hiring engineers who can build these systems.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. NVIDIA offers this as a completely free course—no hidden fees, no paywall for the certificate.</p>

<h3>How long will it take to complete?</h3>
<p>Plan for approximately 8 hours of hands-on work. Most learners complete it over 2–3 weeks by dedicating a few hours per week, though you can move faster if your schedule allows.</p>

<h3>Will I get a certificate?</h3>
<p>Yes. You'll earn a certificate of completion from NVIDIA DLI once you finish the course, which you can add to your resume and LinkedIn profile.</p>

NVIDIA DLI hands-on course on building retrieval-augmented generation agents — embeddings, vector stores, LangChain.

Building RAG Agents with LLMs

<h2>About this course</h2>
<p>Retrieval Augmented Generation (<a href="/topics/rag">RAG</a>) is transforming how AI applications work with information. This free NVIDIA DLI course teaches you the fundamentals of RAG—what it is, when to use it, and how to implement it—in just one hour. You'll learn directly from NVIDIA, the company behind the GPU technology powering modern AI, making this an authoritative introduction to a technique that's reshaping AI development worldwide.</p>

<h2>What you'll learn</h2>
<ul>
<li>What Retrieval Augmented Generation is and how it differs from standard LLM approaches</li>
<li>Why RAG matters: when to use it versus other AI techniques for real-world problems</li>
<li>How RAG works under the hood—the core mechanics of retrieval and generation</li>
<li>Practical use cases where RAG improves AI system accuracy and relevance</li>
<li>How RAG reduces hallucinations and keeps AI responses grounded in real data</li>
<li>The role of vector databases and embeddings in retrieval systems</li>
<li>How to think about RAG when building your own AI applications</li>
</ul>

<h2>Who this is for</h2>
<p>This course is designed for anyone curious about how modern AI systems work—whether you're completely new to AI or have some familiarity with <a href="/topics/llm">large language models</a>. You don't need a computer science degree or advanced math background. If you've heard about AI chatbots and want to understand the technology behind smarter, more accurate AI, this is for you.</p>
<ul>
<li><a href="/for/students">Students</a> — build AI literacy that employers value, with hands-on knowledge of a technique central to modern AI engineering</li>
<li>Career changers — gain practical understanding of RAG to pivot toward AI roles without starting from zero</li>
<li><a href="/for/developers">Developers</a> — learn when and how to integrate RAG into applications you're building</li>
<li>Product <a href="/for/managers">managers</a> — understand the capabilities and limitations of RAG-powered features you're planning</li>
<li>Entrepreneurs — see how RAG enables new AI product ideas and competitive advantages</li>
</ul>

<h2>Prerequisites</h2>
<p>None — this course is beginner-friendly. You don't need prior coding experience, machine learning knowledge, or familiarity with large language models. Just bring curiosity and an hour of your time.</p>

<h2>Why this matters for Indian learners</h2>
<p>RAG is reshaping AI hiring in India. Companies like Flipkart, Infosys, TCS, and Indian AI startups are building RAG systems for customer service, content generation, and decision-making tools. Learning RAG now—directly from NVIDIA—gives you a significant edge in job interviews and contract opportunities, especially as AI roles in India increasingly demand hands-on knowledge of advanced techniques, not just theory.</p>
<p>India's tech sector is racing to adopt AI, and salaries for AI engineers with specialization in advanced techniques like RAG are commanding premiums. This free course is a low-risk way to test your interest and build credentials that directly matter for roles in Bangalore, Hyderabad, and beyond.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. NVIDIA offers this course completely free—no hidden fees, no paid upgrades required. You get the full learning experience at no cost.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed to take about 1 hour. You can complete it in one sitting, or spread it across a week at your own pace. Either way, it's a quick, focused deep dive into RAG fundamentals.</p>
<h3>Will I get a certificate?</h3>
<p>Yes. You'll earn a certificate of completion from NVIDIA when you finish, which you can add to your LinkedIn and resume as proof of your RAG knowledge.</p>

NVIDIA DLI short free course on RAG basics — what it is, when to use it, and how it works.

Augment your LLM Using Retrieval Augmented Generation

<h2>About this course</h2>
<p>This NVIDIA course teaches you how to accelerate your data science workflows using RAPIDS — without changing a single line of code. If you work with pandas and scikit-learn, RAPIDS lets you run the same code on GPUs instead of CPUs, delivering 10-50x speedups on data processing and machine learning tasks. NVIDIA's decades of GPU expertise distill into a practical, hands-on approach that makes GPU acceleration accessible to everyone.</p>

<h2>What you'll learn</h2>
<ul>
<li>How RAPIDS replaces pandas and scikit-learn with GPU-accelerated equivalents while keeping your code unchanged</li>
<li>Techniques to profile and benchmark your workflows to identify bottlenecks worth accelerating</li>
<li>Real-world patterns for loading, cleaning, and exploring large datasets 10-50x faster on GPUs</li>
<li>How to train machine learning models with scikit-learn-compatible APIs running on GPU hardware</li>
<li>Best practices for integrating RAPIDS into existing data pipelines and Jupyter notebooks</li>
<li>When and why GPU acceleration makes sense for your specific use case</li>
</ul>

<h2>Who this is for</h2>
<p>You're a data analyst, engineer, or scientist who works with pandas and scikit-learn and wants to speed up slow pipelines without rewriting everything. Whether you're processing gigabytes of data or training models on tight timelines, this course shows you how to unlock your GPU's power with minimal friction.</p>
<ul>
<li><strong><a href="/for/data-scientists">Data scientists</a></strong> — slash model training time and experiment faster with GPU-accelerated scikit-learn workflows</li>
<li><strong>Data engineers</strong> — process and transform data pipelines 10-50x faster using GPU-native pandas replacements</li>
<li><strong>Analytics professionals</strong> — accelerate exploratory data analysis and reporting on large datasets without learning new tools</li>
<li><strong>Machine learning engineers</strong> — integrate GPU acceleration into production systems while maintaining code compatibility</li>
</ul>

<h2>Prerequisites</h2>
<p>You should be comfortable writing Python code and familiar with pandas DataFrames and scikit-learn basics (training models, splitting data). No GPU experience needed — this course assumes you're new to GPU computing.</p>

<h2>Why this matters for Indian learners</h2>
<p>India's data science talent pool is growing fast, but so is the volume of data companies need to process — banks, fintech startups, e-commerce platforms, and analytics firms increasingly run workloads that demand faster compute. GPU acceleration is now table stakes at companies like Flipkart, Amazon India, and BYJU'S, where teams handle millions of transactions and terabytes of data daily. Learning RAPIDS positions you as someone who can solve expensive scaling problems, making you more valuable in interviews and on the job.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. No hidden fees, no paid certificate upsell — just register, watch, and learn.</p>
<h3>How long will it take to complete?</h3>
<p>The course takes about 1 hour to complete. Most learners finish it in a single sitting or spread it across 1-2 evenings alongside other work.</p>
<h3>Will I get a certificate?</h3>
<p>Yes, you'll earn a certificate of completion from NVIDIA upon finishing the course, which you can add to your LinkedIn profile.</p>

NVIDIA's free course on accelerating pandas/sklearn workflows with RAPIDS — zero code changes needed.

Accelerate Data Science Workflows with Zero Code Changes

NVIDIA DLI's free self-paced introduction to generative AI concepts, applications, and the challenges and opportunities of the field. Foundational for anyone new to GenAI.

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9 free AI courses from Google — covering generative AI, LLMs, prompt engineering, ML basics, and more.

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NVIDIA — Free AI Courses

Generative AI Explained

Building RAG Agents with LLMs

Augment your LLM Using Retrieval Augmented Generation

Accelerate Data Science Workflows with Zero Code Changes

Generative AI Explained

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AI

AIshala

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NVIDIA — Free AI Courses

Generative AI Explained

Building RAG Agents with LLMs

Augment your LLM Using Retrieval Augmented Generation

Accelerate Data Science Workflows with Zero Code Changes

Generative AI Explained

AI

AIshala

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Learn

Community

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