AI Engineering (Batch-05)

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Course Content

Introductory Class

  • AI Engineering (Batch-05) – Introductory Session
    02:38:51

Class 01 – Python Fundamentals: Installation & Configuration | Exploring Jupyter Notebook & VS Code | Python Data Types – Integers & Strings | Built-in Functions | String Indexing & Slicing

Class 02 – Python Fundamentals: Python Data Types – Lists, Tuples, Sets & Dictionaries | Essential Built-in Functions | Comparison & Chained Comparison Operators | Conditional Statements (if, elif, else) | User Input Handling | For Loops

Class 03 – Python Fundamentals: Functions – User-Defined Functions | Making Simple Apps: Hangman | Rock Paper Scissors | Multiplication Table Generator | Budget Tracker | And much more using only Python fundamentals!

Class 04 – Python Real-World Applications: Projects: QR Code Generator | Nearby Location Finder | Dynamic Email Sender | Audio Transcription & Translation | Web Scraper

Class 05 – Python Real-World Applications: PDF Tools – Reading, Text & Image Extraction | Merging | Watermarking | Exploring Libraries – Pandas | Importing & Exporting CSV and Excel Files | Built-in Functions of Pandas | Why Pandas is Important for Data Analysis | Case Study (Part 1): EDA – 365 Men Clothing Store

Class 06 – Exploring Libraries: Pandas – Case Study (Part 2): EDA – 365 Men Clothing Store | GroupBy Operations | Filters | Merging DataFrames

Class 07 – Exploring Libraries: Auto EDA – YData Profiling | Sweetviz | D-Tale | Introduction to NumPy | Creating 1D, 2D & 3D Arrays | NumPy Built-in Functions | Array Operations & Practical Examples

Class 08 – Generative AI Fundamentals: What is Generative AI? | How LLMs Work (Basic Intuition) | Exploring ChatGPT & OpenAI | GROQ API | DeepSeek API | Understanding Tokens (Input & Output Tokens) | Model Parameters & Temperature | Practical API Demonstration

Class 09 – Generative AI – Prompt Engineering: What is Prompt Engineering? | Zero-Shot Prompting | One-Shot Prompting | Few-Shot Prompting | Chain of Thought (CoT) | Self-Consistency | ReAct Framework | Structured Prompt Design | Practical Hands-On Examples

Class 10 – Generative AI – LangChain Framework (Part 1): What is LangChain? | LangChain Architecture Overview | Models / LLM Integration | Prompt Templates | Output Parsers | Chains | LCEL (LangChain Expression Language) | Step-by-Step Implementation

Class 11 – Generative AI – LangChain Framework (Part 2): LangChain Memory | Types of Memory: Chat History | Conversation Buffer | Sliding Window Memory | Summary Memory | Practical Implementation | What is RAG? | RAG Architecture Understanding

Class 12 – Generative AI – Real-World Use Cases: Project 01: Marketing Campaign & LinkedIn Job Post Generator using GROQ API | Project 02: DeepSeek Conversational Chatbot | API Integration | Prompt Optimization

Class 13 – Generative AI – Hugging Face: What is Hugging Face? | Hugging Face Ecosystem Overview (Models, Datasets, Spaces) | What is Inference? | Inference vs Model Download (Local Deployment) | Using Hugging Face Inference API | Hands-On Demo with Meta LLaMA Models & Text-to-Image Generation | Course Recap & Student Experience Discussion

Class 14 – Generative AI: RAG Chatbot with FAISS (Streamlit): Project: RAG-Based Chatbot using Streamlit & Groq API | RAG Architecture (Detailed Code Explanation) | Word Embeddings | Text Splitting & Chunking | FAISS Working | Handling Greeting Messages in RAG

Class 15 – SQL Fundamentals: Introduction to Databases | SQL Installation (SSMS & SQL Server) | Restoring Database Backup | SELECT Statement | Filtering (WHERE, AND, IN, BETWEEN, LIKE) | ORDER BY | String Functions (LEN, UPPER, LOWER, LEFT, RIGHT)

Class 16 – SQL: Query Writing & Joins: Advanced Filtering (AND, OR, NULL, NOT NULL) | SQL Joins (INNER, LEFT, RIGHT, OUTER) | Primary & Foreign Keys | Relationships in SQL

Class 17 – SQL Advanced Concepts: Query Practice | SQL Views Creation | Database Objects (Schema, Sequences, Triggers, Synonyms, Indexes, Procedures) | Aggregation Functions

Class 18 – Generative AI: RAG Chatbot with Flask & LlamaIndex: Virtual Environment Setup | RAG Chatbot using Flask & LlamaIndex | Professional UI | Chat Download Feature | Document Upload/Delete | Conversation Memory | Code Walkthrough

Class 19 – Generative AI: Text-to-SQL Chatbots: Project 1: Basic Text-to-SQL Chatbot (Flask + DB Integration) | Convert User Input to SQL & Fetch Results | Project 2: Advanced Production-Level Text-to-SQL | UI with Schema | AI Insights | Query Explanation | SQL Metrics | Export Functionality

Class 20 – Agentic AI: CrewAI Projects: Introduction to Agentic AI | CrewAI Framework & Components | Project 1: Trip Planner (Destinations, Budgeting, Itinerary, Booking Assistance) | Project 2: Rental Property Finder (Top Listings Worldwide)

Class 21 – Agentic AI: Voice & LangGraph Systems: AI Voice Assistant (Web Search + Response) | LangGraph Framework Overview | Project: Call Center Analysis (Customer vs Agent) | Call Summary | Issue Detection | Abusiveness Detection | Next Action Suggestions

Class 22 – Machine Learning Fundamentals: Introduction to ML | Types (Supervised, Unsupervised, Reinforcement) | Logistic Regression Implementation | Feature Scaling (Normalization vs Standardization)

Class 23 – Machine Learning Workflow: End-to-End ML Pipeline | Data Preprocessing | Feature Engineering | Model Selection | Training & Evaluation | Class Imbalance | Bias-Variance Tradeoff | Underfitting & Overfitting

Class 24 – Machine Learning: Evaluation Metrics: Classification Metrics (Accuracy, Precision, Recall, F1 Score) | Confusion Matrix | ROC Curve & AUC

Class 25 – Machine Learning: Supervised Learning Models: KNN Algorithm (IRIS Dataset Implementation) | Decision Tree (Concept)

Class 26 – Machine Learning: Advanced Models: Decision Tree (Manual Entropy & Information Gain vs Library) | Random Forest | K-Means Clustering | Elbow Method

Class 27 – Machine Learning: Unsupervised Learning: DBSCAN & HDBSCAN (Explanation & Implementation) | Association Rule Mining (Explanation & Implementation) | General Discussion

Class 28 – Deep Learning: Introduction to Neural Networks | Neurons, Input/Hidden/Output Layers | Single & Multi-Layer Perceptron Theory & Implementation | Activation Functions & Backpropagation