Dr Dattas Academy | GENERATIVE AI CERTIFICATION COURSE

Generative AI Certification Course

Generative AI Certification Course:

The Generative AI Certification Course is crucial for aspiring professionals as it covers foundational topics like ML, DL, text analytics, and NLP. It builds expertise in advanced LLM concepts through practical applications, preparing learners for real-world challenges. The capstone project, involving creating a chatbot with enterprise data using Open Source LLMs reinforces practical skills essential for industry readiness. This course is pivotal in equipping participants with in-depth knowledge of natural language processing and text mining, pivotal for launching a successful career in machine learning and AI-driven applications.

Outcome:

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a certificate

Program Structure:

Module	Content	Duration
Module 1 - Basics of ML and DL	Introduction to Machine Learning What is Machine Learning Types of Machine Learning Machine Learning Algorithms Applications of Machine Learning ML vs DL vs AI	2 Hours
Module 2 - Text Analytics and NLP	Introduction to Text Mining Natural Language Toolkit Library Text Extraction and Preprocessing Structuring Sentence Demo	2 Hours
Module 3 - Generative AI Use Cases, Project Lifecycle, and Model Pre-training	Introduction to Generative AI Generative AI Applications Understanding Probability and Statistics in Generative AI Introduction to Generative Models Deep Learning for Generative Models Introduction to Generative Adversarial Networks (GANs) Autoencoders Transformers and Attention Mechanisms - "Attention is all you need" Introduction to Large Language Models (LLMs) Architecture of Large Language Models Text AI LLMs (GPT-3, GPT-4, LaMDA, LLaMA, Stanford Alpaca, Google FLAN, Poe, Falcon LLM) LLM Use Cases and Tasks Logic of LLM Application Value Propositions of LangChain Components of LangChain Benefits of Components Based Approach Off-the-Shelf Chains in LangChain Build and Deploy LLM-Powered Applications Using LangChain Design Your LLM Workflow and Other Steps Aligning Models with Human Values Prompting and Prompt Engineering Generative Configuration Generative AI Project Lifecycle Pre-training Large Language Models Computational Challenges of Training LLMs	4 Hours
Module 4 - Fine-tuning and Evaluating Large Language Models	Introduction Instruction Fine-tuning Fine-tuning on a Single Task Multi-task Instruction Fine-tuning Model Evaluation Benchmarks Parameter Efficient Fine-tuning (PEFT) PEFT Techniques 1: LoRA PEFT Techniques 2: Soft Prompts	4 Hours
Module 5 - Reinforcement Learning and LLM-Powered Applications	Reinforcement Learning from Human Feedback (RLHF) RLHF: Obtaining Feedback from Humans RLHF: Reward Model RLHF: Fine-tuning with Reinforcement Learning Optional Video: Proximal Policy Optimization RLHF: Reward Hacking Scaling Human Feedback Model Optimizations for Deployment Using the LLM in Applications Helping LLMs Reason and Plan with Chain-of-Thought Program-Aided Language Models (PAL) ReAct: Combining Reasoning and Action	3 Hours
Capstone Project	Fundamentals of Python Programming Build Your Chatbot with Enterprise Data Usage Using LangChain & Open Source LLM	15 Hours