At Ameya Edge, we harness the power of AI to drive business innovation and reshape education for a smarter tomorrow. Our solutions help businesses make smarter, faster decisions to streamline operations, boost productivity & improve customer experiences.
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Foundational Course
This comprehensive training program introduces participants to the fundamentals of Generative AI, covering data, artificial intelligence (AI), machine learning (ML), and deep learning. It delves into key concepts, architectures, and business applications of Large Language Models (LLMs), equipping learners with practical skills to build AI-powered solutions.
Introduction to Data, Artificial Intelligence (AI), and Python
This module provides a strong foundation in AI, starting with an overview of data fundamentals and their role in AI systems. Participants will explore the evolution of AI, different learning paradigms (supervised, unsupervised, and reinforcement learning), and gain hands-on experience with Python, the primary programming language used in AI development.
Foundation for Generative AI
This section introduces the core technologies and methodologies that power Generative AI, helping participants understand its evolution and applications.
An overview of the current Gen AI ecosystem, including key players, breakthroughs, and market trends. Participants will learn about the impact of AI in various industries, from content generation to automation.
A deep dive into Machine Learning principles, covering data preprocessing, model training, feature engineering, and evaluation techniques. The session provides a strong understanding of how ML models contribute to Generative AI.
This topic covers neural networks, backpropagation, and advanced architectures like CNNs and RNNs, leading to the foundations of Generative AI models. Participants will explore how Deep Learning enables AI to generate human-like content.
Business Applications with Large Language Models (LLMs)
This module explores the practical use of LLMs in business, from text generation to knowledge retrieval and automation.
An introduction to how transformer-based models like GPT and BERT enable text completion, summarization, and conversational AI. Participants will build and fine-tune their own text generation models.
Learn the art of writing effective prompts to maximize the capabilities of Large Language Models. This session covers zero-shot, one-shot, and few-shot learning techniques, allowing participants to improve AI-generated responses.
Explore how to enhance LLMs with external data sources for more accurate and up-to-date responses. This session demonstrates how RAG improves AI-powered search, chatbots, and recommendation systems.
Building AI Solutions with Generative AI
Participants will apply their knowledge to build practical AI-powered solutions, tackling real-world problems with custom AI workflows.
A hands-on session on customizing pre-trained AI models for specific use cases, such as industry-specific chatbots, document summarization, or personalized content generation.
An introduction to AI agents and automation, where participants learn how to integrate LLMs with APIs, databases, and external tools to create autonomous AI-driven workflows.
A critical discussion on bias, fairness, and security risks in AI. Participants will explore strategies to mitigate bias, ensure ethical AI deployment, and secure LLM-based applications against adversarial attacks and misuse.
Gain hands-on experience with Python, ML, and Generative AI models
Understand the business applications and real-world impact of LLMs
Learn how to fine-tune AI models and integrate them into real-world solutions
Master prompt engineering and retrieval-augmented generation
Develop awareness of ethical AI practices and security considerations
