AI with Python & Generative AI

Course Summary

Our AI with Python & Generative AI Course is designed to provide you with a strong foundation in Python programming, Artificial Intelligence, Machine Learning, and modern Generative AI technologies.
Throughout the course, you will learn how to develop intelligent applications using Python, analyze and visualize data, build machine learning models, and create AI-powered solutions using Large Language Models (LLMs). You will gain hands-on experience with Prompt Engineering, OpenAI APIs, LangChain, Retrieval-Augmented Generation (RAG), and chatbot development while working on real-world projects. The course also covers deployment, security best practices, and industry-standard workflows, preparing you for careers in AI, Generative AI, and intelligent software development.

Learn AI with Python & Generative AI

Syllabus :
Python Fundamentals
( 11)

Introduction to Python
Python Installation and Setup
Variables and Data Types
Operators and Expressions
Input and Output Operations
Conditional Statements
Loops (For and While)
Functions and Modules
Exception Handling
File Handling
Object-Oriented Programming (OOP)

Syllabus :
Advanced Python
( 8)

Lists, Tuples, Sets and Dictionaries
Lambda Functions
List Comprehensions
Regular Expressions
Date and Time Handling
Working with JSON Data
Virtual Environments
Package Management with Pip

Syllabus :
Data Analysis
( 8)

Introduction to Data Analysis
NumPy Fundamentals
Pandas Data Processing
Data Cleaning Techniques
Data Visualization with Matplotlib
Data Visualization with Seaborn
Working with CSV Files
Working with Excel Files

Syllabus :
Machine Learning
( 10)

Introduction to Machine Learning
Machine Learning Workflow
Data Preprocessing
Feature Engineering
Training and Testing Models
Regression Algorithms
Classification Algorithms
Clustering Techniques
Model Evaluation Metrics
Scikit-Learn Fundamentals

Syllabus :
Deep Learning
( 8)

Introduction to Deep Learning
Neural Networks Basics
TensorFlow Fundamentals
Keras API
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Model Optimization Techniques

Syllabus :
NLP
( 8)

Introduction to Natural Language Processing
Text Preprocessing
Tokenization
Stemming and Lemmatization
Sentiment Analysis
Text Classification
Named Entity Recognition (NER)
NLP with Python Libraries

Syllabus :
Generative AI
( 6)

Introduction to Generative AI
Evolution of Large Language Models (LLMs)
Understanding GPT Models
Transformer Architecture
Generative AI Applications
AI Ethics and Responsible AI

Syllabus :
OpenAI LLM
( 7)

Introduction to OpenAI API
API Authentication
Chat Completions
Function Calling
Structured Outputs
Building AI Applications
Cost Optimization

Syllabus :
RAG with LangChain
( 7)

Introduction to LangChain
Prompt Templates
Chains and Agents
Embeddings
Vector Databases
Retrieval Augmented Generation (RAG)
Knowledge Base Chatbots

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