Generative AI Course

The Generative AI for Business is a comprehensive 16-week online learning program offered by Microsoft Azure OpenAI. This program is designed to equip you with the knowledge and skills to leverage the power of Generative AI, Prompt Engineering and Large Language Models to solve real-world business problems.

Key Program Elements:
 

• Foundational Learning: Gain a solid understanding of Generative AI concepts and their applications across diverse business scenarios.

• LLM Fundamentals: Explore the core functionalities of LLMs and how to utilize them effectively.

• Prompt Engineering: Craft effective prompts to guide LLMs and generate desired outputs, both with and without coding.

• Hands-on Learning: Deepen your knowledge through practical exercises, 8+ case studies, and 4 project development activities using the Azure cloud platform, and get the “Microsoft Applied Skill Badge.”

• Azure OpenAI Integration: Learn to leverage Azure OpenAI Studio, APIs, and Python SDKs to build data-driven services within the Azure environment.

• Career Advancement: Pursue an optional 4-week elective focused on core Azure AI functionalities, preparing you for the Azure AI Fundamentals offering AI 900 certification – a valuable asset for career growth in AI.

Learning Methodology:

The program emphasizes a "learning by doing" approach, fostering practical skills development through real-world case studies and project building. This hands-on experience equips you with a portfolio that demonstrates your capabilities and aids your transition into high-demand fields like data science and artificial intelligence.

This Azure OpenAI course offers a unique blend of comprehensive training, practical application, and career-oriented benefits:
 

• Extensive Hands-on Learning: Go beyond theory with industry-oriented 8+ hands-on case studies and 4 dedicated projects. This immersive experience allows you to solidify your understanding and build a portfolio showcasing your real-world Generative AI skills.

• Industry-recognized Certification Preparation: Gain valuable preparation for the sought-after AI-900: Azure AI Fundamentals certification delivered by Microsoft Certified Trainers. This Microsoft Generative AI certificate validates your knowledge and strengthens your resume for AI-focused careers.

• Practical Skill Development: Thoroughly understand prompt engineering, a crucial skill for working with LLMs. This course empowers you to craft effective prompts, both with or without coding, unlocking the full potential of Generative AI tools.

• Diverse Generative AI Applications: Explore practical applications of Generative AI through modules on Text Classification, Summarization, and Generation. This equips you with a versatile skillset applicable to various business scenarios.

• Real-world Development Environment: Gain practical experience working within the Azure cloud platform. You will have access to Microsoft Azure Labs with OpenAI Studio, allowing you to experiment and build Generative AI solutions in a simulated environment.

• Career-Boosting Credentials: Upon completion, you will receive a Certificate of Completion jointly issued by Great Learning and Microsoft. Additionally, you will earn a valuable Microsoft Applied Skills badge in "Develop GenAl Solutions with Azure OpenAI Service," further enhancing your professional profile.

• Comprehensive Support: Throughout the program, you will benefit from a dedicated program manager and academic support from Great Learning to ensure your learning experience is smooth and successful.

This combination of in-depth learning, practical exercises, industry-recognized credentials, and career-oriented resources makes this Azure OpenAI course an exceptional opportunity to propel your skillset and advance your career in the exciting field of Generative AI.

This Microsoft AI course equips you with a comprehensive understanding of Generative AI and its practical applications in business. 

Here's a breakdown of the key learning outcomes:
 

• Foundational Generative AI Knowledge: Gain a solid grasp of GAI's history, current landscape, and future potential. Learn how to practically apply this technology to solve real-world problems and build impactful services.

• Mastering the Microsoft Azure OpenAI Platform: Leverage the complete potential of the Microsoft Azure OpenAI platform to utilize Generative AI capabilities effectively.

• Scaling Prompt Engineering: Explore leveraging APIs and Python SDKs to scale your prompt engineering efforts, ensuring efficiency and effectiveness.

• Business-Oriented Prompt Engineering: Develop expertise in crafting prompts specifically designed for various business use cases. This allows you to extract maximum value from Generative AI solutions.

• Practical Generative AI Applications: Gain a working knowledge of how to apply Generative AI for core business tasks like natural language classification, summarization, and generation.

• Large Language Model Optimization: Understand how to fine-tune LLMs to achieve desired outputs, ensuring your GAI solutions deliver accurate and relevant results.

• Enterprise-Level GAI Thinking: Develop a strategic perspective on implementing Generative AI solutions within an enterprise environment.

• Hands-on Coding Skills: Learn to write basic code snippets that interact with LLM APIs and enable large-scale prompt engineering. This equips you with the practical skills to build and deploy GenAI solutions.

By completing this course, you will develop a well-rounded understanding of Generative AI, its business applications, and the technical skills to implement it effectively within your organization.

This program takes a structured, modular approach to learning, ensuring a progressive development of your Generative AI expertise. 

Here's a breakdown of the four distinct modules:
 

• Module 1: Foundational Knowledge

This module establishes a strong base by introducing core concepts of Artificial Intelligence, Machine Learning, Large Language Models, and Prompt Engineering.

Additionally, you will gain a comprehensive overview of Microsoft Azure's OpenAI services, familiarizing you with the available tools and functionalities.
 

• Module 2: Python Programming for GAI

This module focuses on developing essential Python programming skills. Python is a widely used language for working with Generative AI, and this module equips you to handle large-scale GenAI applications effectively.
 

• Module 3: Hands-on Generative AI Applications

This hands-on module provides practical experience with the Azure OpenAI API key and Python SDK. You will explore real-world applications of Generative AI, exploring tasks like text classification and summarization.
 

• Module 4: Preparing for AI-900 Certification (Optional)

The final module focuses on preparing you for the sought-after AI-900 certification exam. This optional module validates your understanding of core Azure AI functionalities and strengthens your AI career prospects.

This structured learning journey ensures a strong foundation in core concepts. It is followed by practical application through hands-on exercises, culminating in the opportunity to earn a valuable industry credential.

This Microsoft OpenAI certificate course incorporates engaging projects that enable you to apply your knowledge to practical business scenarios. 

Here are a few examples:
 

• Product Review Analysis: Develop a system for analyzing product reviews using sentiment analysis. This project will involve creating prompts to extract key information like product names, ratings, and customer sentiment, helping companies gain valuable insights from customer feedback.

• Aspect-Based Sentiment Analysis: Take sentiment analysis a step further by identifying specific aspects of a product or service that customers are happy or dissatisfied with. This project equips you with the skills to help businesses understand customer needs and make data-driven decisions for improvement.

• Optimizing Logistics with Generative AI: Explore how Generative AI can address the challenges faced by logistics companies, potentially improving delivery efficiency and customer satisfaction.
   

Extracting Insights from E-commerce Feedback: Learn to harness Generative AI to analyze unstructured customer feedback in the e-commerce industry. This project equips you with skills to gain valuable insights for optimizing user experience and driving business growth.

The Microsoft AI certificate course costs INR 1,20,000 + GST. For more details on flexible fee payments, please contact your Program Advisor.

The skills you gain from this Generative AI for Business with Microsoft Azure OpenAI course can be applied across various industries and job functions. 

Here are some potential areas where your expertise can be valuable:
 

• Data Science and Machine Learning: This course strengthens your foundation in core AI concepts like Machine Learning and Large Language Models, complementing your existing data science skillset.

• Business Intelligence and Analytics: Generative AI offers powerful tools for analyzing vast amounts of data. You can leverage your skills to extract valuable insights for businesses, informing strategic decision-making.

• Content Creation and Marketing: Generative AI has the potential to revolutionize content creation. You can apply your skills to develop creative content strategies, automate tasks, and personalize marketing campaigns.

• Customer Service and Experience: Generative AI can be used to build chatbots and virtual assistants, enhancing customer service interactions. Your skills can be instrumental in developing these solutions to improve customer experience.

• Product Development and Innovation: Generative AI allows innovative product design and development. You can utilize your knowledge to explore new product ideas and functionalities.

Building a Generative AI model involves several key steps:
 

• Define the Goal: Clearly define the problem you want your GAI model to solve. What kind of outputs do you want it to generate (text, code, images, etc.)? What data will it be based on?

• Data Collection & Preprocessing: Gather a high-quality dataset relevant to your chosen task. This data will train the model and shape its ability to generate new outputs. Preprocessing often involves cleaning, organizing, and formatting the data to ensure the model can understand and learn from it effectively.

• Choose the Right Model Architecture: Different GAI model architectures are suited for various tasks.  Some popular options include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. Researching available architectures and their strengths for your specific goal is crucial.

• Model Training: Train your chosen model on the prepared dataset. This can be a computationally intensive process, requiring powerful hardware and potentially taking significant time, depending on the model complexity and dataset size.

• Validation & Refinement: Evaluate your trained model's performance. How well does it generate the desired outputs? Does it meet your quality standards? This iterative process often involves adjusting hyperparameters, the settings that control the model's training process, and potentially refining the model architecture for better results.

• Deployment (Optional): You can deploy your model when its performance is iterated and optimized for real-world use. This might involve integrating it into an application or service that generates outputs based on user input or specific tasks.

Here are some additional points to consider:
 

• Prompt Engineering: Crafting effective prompts is essential for guiding GAI models to generate the desired outputs. Writing clear and concise prompts is a valuable skill for working with generative models.

• Computational Resources: Training GAI models often requires significant computing power. Cloud platforms like Azure OpenAI offer resources and tools to facilitate this process.

• Ethical Considerations: Be mindful of potential biases in your training data and how they might influence the model's outputs. Additionally, the ethical implications of using GAI models, such as potential misuse to generate fake content, should be considered.

Building a GAI model can be a complex process, but with careful planning, the right tools, and a solid knowledge of the core concepts, you can create powerful tools for various applications.