Master cutting-edge Generative AI skills and unlock business growth

Generative AI is a subset of AI that uses Machine Learning models to learn the underlying patterns and structures in large volumes of training data. It then uses that understanding to create new data, such as images, text, videos, and more. This module provides a comprehensive overview of Generative AI models, how they evolved, and how to apply them effectively to various business challenges. 

(History of Generative AI, Generative AI vs Discriminative AI, Interacting with Generative AI, Overview of Hallucination, Business Applications of Generative AI, Overview of Pandas, Pandas DataFrames, Visual Analysis of Data)

Machine Learning, a subset of Artificial Intelligence, focuses on developing algorithms that can learn patterns in data and make predictions. These algorithms do this without being explicitly programmed. This module introduces participants to the concept of learning from data. It covers what ML is, its types, and how to train and evaluate ML models for business applications. 

(The Notion of Learning from Data, Introduction to ML, Types of ML Problem and Solution Space for ML, Exploratory Data Analysis, Training ML Models, Evaluating ML models)

(Overview of Neural Networks, Neural Network Architecture, Activation Functions, Gradient Descent, Training a Neural Network, Backpropagation)

(The Need for Embeddings, Introduction to Transformers, Encoder Component of a Transformer, Attention and Self-Attention, Sentence Embeddings with Sentence Transformers, Semantic Search Applications)

Industry - Finance 

Summary - Analyze the data comprising stock news and prices and develop an AI-driven sentiment analysis system that will process and analyze news articles to gauge market sentiment to help financial analysts optimize investment strategies and improve client outcomes. 

Tools & Concepts - Google Colab, Hugging Face, Transformers, Sentence Transformers

Decoder-only transformers autoregressively generate text by predicting one word at a time based on previous words. This module will provide learners with a comprehensive view of the decoder component in transformers, including the role of masking, cross-attention, and autoregressive generation. It will also cover how decoder-only transformer models process and generate text, as well as how to apply transformers to real-world business use cases. 

(The Decoder Component of a Transformer, Masking, Cross Attention, Autoregressive Nature of the Decoder, Text Generation Applications)

Large Language Models (LLMs) are ML models that are pre-trained on large corpora of data and possess the ability to generate coherent and contextually relevant content. Prompt engineering is a process of iteratively deriving a specific set of instructions to help an LLM accomplish a specific task. This module introduces LLMs, explains their working, and covers practices to devise prompts to solve problems using LLMs effectively.

(Introduction to LLMs, Working of LLMs, Applications of LLMs, Introduction to Prompt Engineering, Strategies for Devising Prompts)

(External Knowledge Sources, Data Chunking, Vector Databases, RAG, Evaluating RAG Systems)

Industry - Healthcare 

Summary - Utilize sentence embeddings, vector databases, and Retrieval-Augmented Generation (RAG) to enhance information retrieval for a medical chatbot and provide accurate and context-aware responses, ensuring reliable and relevant medical guidance. 

Tools & Concepts - Generative AI, Large Language Models, Prompt Engineering, Hugging Face, Retrieval Augmented Generation, Vector Databases.

(The Need for Fine-Tuning, Parameter-Efficient Fine-Tuning (PEFT), PEFT Techniques (Prefix Tuning, Prompt Tuning, QLoRA), QLoRA Application and Implementation)

(Introduction to AI Agents, Agentic AI Tools, AI Agents within LangChain, Agentic AI Workflows, Types of AI Agents)

(Identifying Bias and Risk in Human and AI Systems, Mitigating Bias and Risk in AI, Laws and Regulations for Responsible AI Use)

Summary - Utilize agentic AI workflows and LangChain to automate document analysis and summarization with an AI-powered legal document synthesizer by leveraging parameter-efficient fine-tuning techniques for efficient adaptation to legal language, mitigating bias, and minimizing risk in AI-generated content. 

Tools & Concepts - Large Language Models, Prompt Engineering, Parameter-Efficient Fine-Tuning, AI Agents, Responsible AI

This course will help you explore how to solve business problems by generating code using Generative AI tools, examine the capabilities of text-to-image and image-to-text GenAI tools like DallE through business use cases, and explore the speech recognition capabilities of audio-to-text GenAI tools like Whisper through business use cases. 

• Image Captioning using GenAI 
• Speech Recognition using GenAI

This course will provide you with an overview of the basic principles of MLOps and LLMOps. You will also explore how to deploy Generative AI solutions effectively using web applications. The course also ensures that the solutions are scalable to wider audiences, helping to solve business problems. 

• Overview of MLOps and LLMOps Deploying 
• Generative AI Solutions using Web Apps