• Fundamentals of AI, ML and generative AI
• AI vs. generative AI: Key differences and capabilities
• Overview of supervised, unsupervised, and reinforcement learning
• Neural networks, deep learning, and large language models (LLMs)
• How LLMs gave rise to intelligent Chatbots
• Temperature settings and sampling techniques for better AI outputs

• Generative AI infrastructure: key components and business impact
• Foundation models (GPT-4, MidJourney, Stable Diffusion) and applications
• Open-source vs. proprietary models: Trade-offs, costs, and flexibility
• LLMOps: Monitoring, security, and ethical safeguards for AI deployment
• AI development tools: LangChain, MindsDB, vector databases, and low-code platforms
• Fine-tuning vs. RAGs: Optimization strategies and cost-performance balance
• Cloud and compute resources: AWS, Google Cloud, Azure, and scaling AI models

• Fundamentals of workflow automation and key benefits
• Zapier: Workflow automation tools, triggers, and Zaps
• Integrating OpenAI API with Zapier for AI-powered automation
• Building and debugging automated workflows with AI-generated content
• Use cases: Content generation, email automation, and social media workflows
• Advanced automation: Multistep Zaps, conditional logic, and integrations
• Monitoring, optimization, and scaling AI-powered workflows

• The evolving role of AI and ML in sales and marketing strategies
• Lead generation, customer segmentation, and personalized campaigns using AI
• Predictive analytics for sales forecasting, pipeline management, and customer retention
• Automating content creation: AI-generated text, images, and videos for marketing
• Enhancing customer interactions with AI-powered chatbots and virtual assistants
• Real-world case studies: hyper-personalized travel search and smart sales assistants
• Addressing challenges: Data privacy, brand consistency, and technical implementation hurdles

• Impact of generative AI on customer service operations
• Personalizing interactions with AI chatbots and virtual assistants
• Automating problem resolution and dynamic responses
• AI-powered sentiment analysis and customer journey insights
• Case studies: AI virtual assistants and outage management
• Practical considerations: Data privacy, quality, and human touch balance

• The role of AI in modern software development and design
• AI-powered code generation, including completion and translation
• Automating code review, test case generation, and vulnerability identification
• Generating and customizing software documentation with AI
• Refactoring and optimizing code for performance and readability
• Enhancing UI/UX design with AI, focusing on user behavior and accessibility
• Collaborative tools: AI integration in development environments and prototyping
• Future trends in generative AI and no-code tools for software engineering

• Introduction to generative AI in data analytics
• Exploring and discovering insights: Hypothesis generation and trend identification
• Detecting anomalies and outliers and creating interactive data visualizations
• Building predictive models and generating forecasts with AI
• Simulating scenarios to assess risks and opportunities in data modeling
• Real-world use cases: Data analysis optimization and efficiency
• Challenges: Computational constraints, synthetic data ethics, and model stability
   

• AI's impact on the product lifecycle and management
• Automated market research and ideation: Validating product ideas with AI
• Customer journey mapping using predictive analytics
• Auto-generating design variants and virtual prototyping
• AI-assisted SWOT analysis, risk assessment, and continuous design improvement
• Enhancing UX with adaptive features and predictive behavior modeling
• Data-driven product management: Real-time performance metrics and feature prioritization
• Ethical considerations, data privacy, and aligning AI with brand strategy
   

• Initial AI costs: Data, model development, and cloud setup
• Ongoing costs: Compute resources, maintenance, and personnel
• Unit economics: AI vs. manual cost comparison and scalability
• AI ROI: Calculating investment, efficiency, and trade-offs
• Ethical concerns: Bias, fairness, privacy, and accountability
• Data privacy: GDPR, CCPA, and compliance challenges
• Future directions: Collaboration on AI ethics with governments

• Overview, key features, and getting started with Copilot
• Enhancing sales with personalized proposals
• Optimizing IT operations with troubleshooting guides
• Powering marketing insights and campaign reports
• Improving financial planning and budget summaries
• Streamlining HR with onboarding checklists
• Optimizing operations with inventory dashboards
• Best practices and key takeaways for Copilot integration

Attend an exclusive online masterclass led by Purdue faculty and staff to gain deep insights into the transformative power of generative AI for business. Explore cutting-edge advancements, real-world business applications, and strategic frameworks that make AI adoption necessary for organizations. Understand how AI is reshaping industries, optimizing decision-making, and driving innovation, equipping you with the knowledge to lead AI-driven business transformation.

This Microsoft course equips participants with the skills to drive user enablement and AI adoption within their organization. It explores the people-first strategy and the Microsoft 365 Copilot user enablement framework, structured around four essential phases: Get Ready (building a strong foundation), Onboard & Engage (empowering employees), Deliver Impact (maximizing value), and Extend & Optimize (expanding capabilities).

Designed an automated workflow using Zapier to generate blog content and send stakeholder email updates, showcasing seamless tool integration for efficient content distribution and communication.

Developed a comprehensive strategy to maintain brand consistency using GenAI. Defined voice, tone, and style guidelines, created AI-generated content, and established a review process to ensure alignment.

Built a GenAI-powered social media campaign promoting an eco-friendly product using ChatGPT and DALL·E. Executed A/B testing and extracted insights to optimize campaign effectiveness.

Created and fine-tuned a customer support agent using Eleven Labs. Focused on ensuring compliance, performance optimization, and reliability through thorough testing and iteration.

Designed and implemented LeadSuccessBot to boost engagement on a job-matching platform, demonstrating the practical impact of AI chatbots in enhancing business interaction.

Formulated a plan to incorporate GenAI into data analytics workflows, improving efficiency and decision-making accuracy while applying ChatGPT in practical scenarios.

Applied Generative AI to a real-world business problem, streamlining data workflows, discovering actionable insights, and delivering decision-ready solutions with enhanced creativity.

Oversaw the full product lifecycle of Fit Smart using ChatGPT—from research and development to launch and retirement—focusing on process efficiency, insight generation, and problem resolution.

Used GenAI tools like ChatGPT and Copilot to automate decisions related to feature scaling, leveraging engagement metrics and regional data to resolve delays and optimize product expansion.

Utilized Microsoft Copilot to support the launch of an e-bike product, generating strategic documents and streamlining communication for cross-functional teams.

Created an engaging, interactive storytelling experience using ChatGPT—enabling the development of custom narratives without the need for coding or external tools.

Generative AI engineers spearhead the creation of advanced AI-driven solutions. Their role includes designing algorithms for generative models using deep learning, fine-tuning for performance, and integrating these models into business workflows. They also communicate technical concepts to non-technical teams and stay updated on the latest AI innovations.

 

The program is led by industry practitioners and AI experts who bring real-world insights to the learning experience. Trainers are rigorously vetted through profile evaluations, technical assessments, and training demos. Purdue University faculty deliver exclusive masterclasses, while AWS curates the self-paced content.

 

• Joint certificate from Purdue University Online and Nvidya
• Access to Purdue’s Alumni Association
• Deep understanding of GenAI’s role in business strategy
• Exposure to real-world applications across functions
• Self-paced content authored by AWS

 

Enrollment is a simple three-step process:
1. Submit an online application detailing your background and goals.
2. An admissions panel will review and shortlist candidates.
3. Selected applicants will receive an email confirmation and can begin within 1–2 weeks upon payment.

 

AI Engineer Annual Salary:
Region Entry-Level Experienced
U.S. $122,000 $192,000
India ₹6 Lakhs ₹14 Lakhs

To apply, candidates should meet the following:
• Minimum 5 years of professional experience
• Proficiency in English (spoken and written)

Yes. The program is 100% online, accessible anytime from desktop or mobile devices. No in-person attendance required.

 

Business leaders can apply GenAI in diverse areas—sales, marketing, software development, customer service, and more—to enhance efficiency and innovation. This course enables a clear understanding of practical applications across business functions.

Yes. Nvidya for Business partners with mid-size and Fortune 500 companies, offering tailored learning paths, certification programs, and unlimited access through the Learning Hub+. Our consultants work directly with teams to align learning with business goals.

 

Generative AI enhances digital transformation by enabling smarter, faster decision-making and automating complex processes. It can help build new business models, optimize supply chains, and foster innovation across an organization.

 

No problem! With our Flexi-Learn option, you can catch up anytime via recorded sessions, ensuring you stay on track toward certification.

Nvidya provides a wide range of programs, including:
• Professional Certificate in Generative AI
• Machine Learning Certification
• Machine Learning Engineer Live Cohorts
• AI Training
• Introduction to Generative AI