Generative Ai Mastery: From Chatgpt To Langchain In Python

Explore Generative AI, ChatGPT, LangChain, Prompt Engineering. Build, Deploy, Optimize GenAI Models Apps with Python

What you'll learn

Hands-on mastery of Gen AI. Working with multiple Generative AI tools- ChatGPT, Stable Diffusion, Llama and more

Advanced prompt engineering for Multimodal engines to achieve precise and useful work from GenAI

Explore different deployment strategies, ensuring scalability and reliability of AI applications.

Interview preperation to secure top jobs in the domain of Gen AI

Work with ChatGPT like a pro with latest models- GPT 4o, DALL-E 3 and others for content creation, data analysis, ideation, social media posts, productivity.

Build apps powered by Gen AI - with APIs and also with AI models running on your computer

Requirements

Familiarity with Python programming language, including basic syntax, data structures, and functions.

Understanding of APIs and how to interact with them, as the course will involve integrating various APIs for model deployment and usage.

Ability to navigate and execute commands in a command line interface (CLI) or terminal.

Description

Unlock the future of Artificial Intelligence with "Generative AI Mastery: From ChatGPT to LangChain in Python"! This comprehensive course is designed to take you from a beginner to an advanced level in Generative AI, focusing on ChatGPT, LangChain, Prompt Engineering, and the latest Large Language Models (LLMs). Whether you are a data scientist, AI enthusiast, or software developer, this course equips you with the skills needed to build, deploy, and optimize Generative AI models and applications using Python.What You Will Learn:Fundamentals of Generative AI: Understand the core concepts, architectures, and applications of Generative AI, including ChatGPT and other cutting-edge models.Prompt Engineering for ChatGPT: Master prompt design and optimization techniques to get the most accurate and efficient outputs from ChatGPT.Large Language Models (LLMs): Dive deep into LLMs, learning how to fine-tune and deploy them for real-world use cases.LangChain Integration: Learn how to leverage LangChain to build advanced AI-driven applications that utilize natural language processing and understanding.Python for Generative AI: Develop and implement AI models with Python. Utilize powerful libraries and frameworks to create scalable AI solutions.Building AI-Powered Applications: Learn end-to-end development, from data preprocessing and model training to deploying and monitoring AI models in production environments.Optimization Techniques: Discover strategies to optimize model performance, reduce latency, and enhance the quality of AI outputs for various applications.Why Enroll in This Course?Hands-on Projects: Gain practical experience by working on real-world projects that will boost your portfolio and demonstrate your expertise in Generative AI.Expert Guidance: Learn from industry experts with years of experience in AI, Machine Learning, and Python development.Community Support: Join a vibrant community of AI learners and professionals to collaborate, share knowledge, and grow together.Stay Ahead in AI: As the field of Generative AI rapidly evolves, this course ensures you stay ahead with the latest trends, tools, and techniques.Who Should Take This Course?Data Scientists and Machine Learning Engineers looking to specialize in Generative AI.Software Developers eager to build AI-powered applications using Python.AI Enthusiasts and Beginners who want to break into the AI field with a strong foundation in Generative AI technologies.Professionals and Students aiming to master ChatGPT, LLMs, Prompt Engineering, and LangChain.Transform your career and become a sought-after expert in Generative AI. Enroll now and start your journey into the exciting world of AI innovation!

Overview

Section 1: Introduction to Generative AI

Lecture 1 Introduction - What you will Learn

Lecture 2 Create Python Environment | Native Python Way

Lecture 3 Create Python Environment | Python distribution Anaconda

Lecture 4 Jupyter Notebook in VS Code

Section 2: Python Basic Fundamentals

Lecture 5 Introduction to Python - Essential Syntax and Semantics I

Lecture 6 Introduction to Python - Essential Syntax and Semantics II

Lecture 7 Python Variables

Lecture 8 Operators in Python

Section 3: Python: Understanding Control Flow

Lecture 9 Conditional Statements

Lecture 10 Loops in Python

Section 4: Understanding Data Structures in Python

Lecture 11 Python Lists and List Comprehension: Everything You Need to Know I

Lecture 12 Python Lists and List Comprehension: Everything You Need to Know II

Lecture 13 Tuples in Python: Immutable Collections

Lecture 14 Python Dictionaries: Efficient Key-Value Pair Management

Lecture 15 Python Dictionaries: Efficient Key-Value Pair Management II

Section 5: Functions in Python

Lecture 16 Exploring Functions in Python

Lecture 17 Exploring Functions in Python II

Section 6: Module Fundamentals: Importing, Creation, and Packaging

Lecture 18 Introduction to Modules

Lecture 19 Importing Modules

Lecture 20 Creating Custom Modules

Lecture 21 Packaging Modules

Lecture 22 Using Third-Party Modules

Section 7: File Handling in Python

Lecture 23 File Operations with Python

Lecture 24 Working with File Paths in Python

Section 8: Exception Handling in Python

Lecture 25 Exception Handling in Python I

Lecture 26 Exception Handling in Python II

Section 9: Python OOPs Concepts

Lecture 27 Python Classes and Objects

Lecture 28 Use of "self" in Python

Lecture 29 Encapsulation in Python

Lecture 30 Inheritance in Python

Lecture 31 Multiple and Multi-Level Inheritance

Lecture 32 Polymorphism in Python

Lecture 33 *args and **kwargs in Python

Lecture 34 Abstraction in Python

Section 10: Create Web Apps for Machine Learning

Lecture 35 Building Interactive Web Apps for Data Science and Machine Learning

Lecture 36 Build App - BMI Calculator

Lecture 37 Build App - ML-Powered App

Section 11: Machine Learning Fundamentals for NLP (Prerequisite)

Lecture 38 Road Map of NLP Learning

Lecture 39 UseCases of the NLP

Lecture 40 Tokenization Essentials and Key NLP Terminologies

Lecture 41 Practical Example of Tokenization

Lecture 42 Text Preprocessing - Stemming

Lecture 43 Text Preprocessing - Lemmatization

Lecture 44 Parts of Speech (POS) Tagging Using NLTK

Lecture 45 Text Preprocessing - StopWords

Lecture 46 Named Entity Recognition (NER)

Lecture 47 One-Hot Encoding in NLP

Lecture 48 Example : One-Hot Encoding in NLP

Lecture 49 Bag of Words (BoW) in NLP

Lecture 50 Application of Bag of Words (BoW)

Individuals passionate about AI and ML who want to expand their knowledge and skills in generative AI applications.,Developers interested in integrating advanced AI capabilities into their applications and learning about the deployment and optimization of AI models.