AI Engineering (Batch-03)

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Course Content

Introductory Class

  • AI Engineering (Batch-03) – Introductory Session
    02:52:20

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: Loops – For & While Loops | Break, Continue & Pass Statements | Mini Project: Guess the Number Game (3 Attempts) | Functions – Lambda & User-Defined Functions

Class 04 – Python Fundamentals: Using Filter() & Map() Functions | Mini Projects: Rock-Paper-Scissors, Simple Calculator, Multiplication Table Generator | Introduction to Python Libraries | Exploring NumPy – Array Creation (1D, 2D, 3D), Indexing, Slicing & Built-in Functions

Class 05 – Python Fundamentals: Exploring Pandas (Part 1) | Data Cleaning, Preprocessing & EDA using Pandas | Automated EDA Tools – YData Profiling, SweetViz & D-Tale

Class 06 – Python Fundamentals: Exploring Pandas (Part 2) | Grouping & Aggregating Data | Combining DataFrames (Merge & Concatenate) | Data Manipulation Techniques | Introduction to Matplotlib – Bar, Line, Scatter, Histogram, Box & Violin Plots

Class 07 – Python Fundamentals: Exploring Seaborn Library | Categorical, Relational & Distribution Plots | Python EDA Case Study: Retail Clothing Brand – Complete Dataset Exploration & Visualization

Class 08 – Python Real-World Applications: Projects: QR Code Generator | Nearby Location Finder | Dynamic Email Sender | Audio Transcription & Translation | PDF Tools – Reading, Text & Image Extraction, Merging, Watermarking

Class 09 – Machine Learning: Introduction to Machine Learning | Structured vs Unstructured Data | Input & Output Variables | Supervised vs Unsupervised Learning | Logistic Regression – Concept & Implementation | Feature Scaling – Standardization & Normalization

Class 10 – Machine Learning: Model Evaluation Metrics for Classification – Confusion Matrix, Accuracy, Precision, Recall, F1 Score (TP, FP, FN, TN) | Overview of ML Models | Linear Regression – Theory & Detailed Explanation

Class 11 – Machine Learning: Linear Regression – Implementation & Evaluation Metrics (MAE, MSE, RMSE) | Decision Tree Algorithm – Concept, Implementation & Use Case

Class 12 – Machine Learning: Class Imbalance Problem – Concept & Impact | Techniques to Overcome Imbalance | Oversampling & Undersampling | SMOTE & Hybrid Techniques (SMOTE + Tomek Links) | Practical Implementation

Class 13 – Machine Learning: Handling Class Imbalance (Part 2) | Heart Disease Prediction Project (Logistic Regression) | Introduction to Support Vector Machines – Concept & Intuition

Class 14 – Machine Learning: Bias–Variance Trade-Off | Overfitting & Underfitting Deep Dive | K-Means Clustering – Concept, Implementation & Use Cases | Elbow Method Explanation

Class 15 – Machine Learning: DBSCAN & HDBSCAN – Density-Based Clustering Techniques | Detailed Concepts, Parameters & Implementation | ML Categorization – General Discussion

Class 16 – Machine Learning: Market Basket Analysis | Apriori Algorithm & FP-Growth Algorithm – Theory, Support–Confidence–Lift Metrics, and End-to-End Implementation

Class 17 – Deep Learning & Generative AI: Concepts Overview | Industry Trends & Technologies | Configuring and Using OpenAI & Groq APIs – Step-by-Step Guidance

Class 18 – Generative AI: Overview of Modern AI Platforms | Detailed Introduction to Hugging Face | What is Inference? | Running Text-to-Text & Text-to-Image Models | Building a Simple Chatbot Using Groq API in Streamlit

Class 19 – Generative AI: Chatbot Development – Detailed Code Walkthrough Using DeepSeek API

Class 20 – Generative AI: LangChain Framework Deep Dive | Components Explained – Models, Prompt Templates, LLM Chains (Simple & Sequential), Memory Module | Conversation Buffer Memory | Introduction to Retrieval-Augmented Generation (RAG)

Class 21 – Generative AI: Prompt Engineering Masterclass | Zero-Shot, One-Shot & Few-Shot Prompting | Chain-of-Thought, Self-Consistency & ReAct Prompting | Practical Working Examples | Why Prompting Matters in AI Systems

Class 22 – Generative AI: RAG Framework – End-to-End Overview | Vector Databases & Embeddings Explained | Word2Vec Conceptual Understanding | How Vector Stores Work