88 Sections
192 Lessons
16 Weeks

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Installation
4
- 1.1
  Install Python from the official website.
- 1.2
  Install Visual Studio Code as your code editor.
- 1.3
  Add Python to system PATH during installation.
- 1.4
  Use the terminal or command prompt to verify installation with python –version.
Language Overview
6
- 2.1
  Python is interpreted, high-level, and dynamically typed.
- 2.2
  print() is used for output; indentation defines blocks.
- 2.3
  Statements: instructions Python executes (e.g., x = 5).
- 2.4
  Expressions: values combined using operators (e.g., x + y).
- 2.5
  Comments start with #; multi-line with triple quotes (”’).
- 2.6
  Code structure depends on proper indentation (whitespace matters).
Types and Values
5
- 3.1
  Basic types: int, float, str, bool.
- 3.2
  type() shows the data type; id() returns the memory address.
- 3.3
  Sequence types: list, tuple, range.
- 3.4
  Strings are immutable; concatenation with +.
- 3.5
  Type conversion: str(), int(), float(), bool().
Conditionals
4
- 4.1
  Syntax: if, elif, else.
- 4.2
  Logical operators: and, or, not.
- 4.3
  Comparison operators: ==, !=, >, =, <=.
- 4.4
  Ternary operator: x if condition else y.
Loops
4
- 5.1
  while loop: runs as long as a condition is True.
- 5.2
  for loop: iterates over sequences (list, str, range()).
- 5.3
  Use break, continue, and pass for control flow.
- 5.4
  range(start, stop, step) used in numeric iteration.
Operators
5
- 6.1
  Arithmetic: +, -, *, /, %, //, **.
- 6.2
  Bitwise: &, |, ^, ~, <>.
- 6.3
  Comparison: ==, !=, >, =, <=.
- 6.4
  Boolean: and, or, not.
- 6.5
  Operator precedence determines execution order.
Functions
5
- 7.1
  Defined using def; can return values with return.
- 7.2
  Positional, keyword, default, and variable-length arguments.
- 7.3
  Functions can return multiple values as tuples.
- 7.4
  Use *args for variable arguments, **kwargs for keyword arguments.
- 7.5
  Generators use yield for memory-efficient iteration.
Structured Data
6
- 8.1
  Use import to bring in built-in or custom modules.
- 8.2
  Common modules
- 8.3
  math: mathematical functions.
- 8.4
  sys: system-specific functions and variables.
- 8.5
  time: time-related functions.
- 8.6
  Use dir(module) to explore module contents.
String Objects
4
- 9.1
  Strings are immutable sequences of Unicode characters.
- 9.2
  Methods: .lower(), .upper(), .replace(), .find(), .strip().
- 9.3
  Splitting and joining strings with .split() and .join().
- 9.4
  Formatting: f”Hello, {name}” or “{}”.format(value).
File I/O
5
- 10.1
  Open files with open(‘filename’, ‘mode’).
- 10.2
  Modes: ‘r’, ‘w’, ‘a’, ‘rb’, ‘wb’.
- 10.3
  Use with open() as file: to auto-close files.
- 10.4
  Read/write using .read(), .write(), .readlines().
- 10.5
  Handle command-line arguments using sys.argv.
Python: What's Built-In
4
- 11.1
  Use min(), max(), sum(), len(), sorted() for basic operations.
- 11.2
  Type functions: type(), isinstance().
- 11.3
  range() generates a sequence of numbers.
- 11.4
  abs(), round(), pow() for numeric manipulation.
Modules in Python
4
- 12.1
  Standard Modules – sys
- 12.2
  Standard Modules – math
- 12.3
  Standard Modules – time
- 12.4
  The Dir Function
Exceptions
5
- 13.1
  Use try, except to handle runtime errors.
- 13.2
  Optionally use else and finally blocks.
- 13.3
  Catch specific exceptions (e.g., ZeroDivisionError, ValueError).
- 13.4
  Use raise to manually trigger exceptions.
- 13.5
  Use assert to enforce conditions during debugging.
OOP, RegEx & Databases in Python
This module focuses on building structured applications using OOP, automating data validation with Regular Expressions, and managing structured data through database integration. Students design reusable components and connect Python applications with SQL databases to develop backend solutions.
0
Key Business Questions
3
- 15.1
  How can we build reusable software modules with OOP principles?
- 15.2
  How can we validate customer input (emails, IDs, phone numbers) automatically?
- 15.3
  How can we store, update, and retrieve large datasets efficiently using Python?
Object-Oriented Programming (OOP)
4
- 16.1
  Classes & Objects
- 16.2
  Inheritance & Polymorphism
- 16.3
  Attributes & Methods
- 16.4
  Encapsulation & Abstraction
Working with Regular Expressions (RegEx)
4
- 17.1
  Pattern Matching: match(), search(), findall()
- 17.2
  Introduction to RegEx in Python (re module)
- 17.3
  Character Classes, Quantifiers, and Groups
- 17.4
  Validating Emails, Phone Numbers, and IDs
Databases with Python
5
- 18.1
  Introduction to Databases & SQL
- 18.2
  SQLite with Python (sqlite3 module)
- 18.3
  Creating Tables & Inserting Data
- 18.4
  Handling Transactions & Errors
- 18.5
  Querying, Updating, and Deleting Records
Projects
3
- 19.1
  Student Management System (OOP + Database)
- 19.2
  Email & Phone Validator (RegEx)
- 19.3
  Expense Tracker with SQLite (CRUD operations)
Python Advanced Libraries
Advanced Python libraries power machine learning, deep learning, NLP, and computer vision. Tools like Scikit-learn, TensorFlow, and PyTorch enable efficient model development and scalable AI solutions.
0
Libraries
0
Scikit-learn
1
- 22.1
  Implements classical machine learning algorithms for regression, classification, clustering, and model evaluation.
TensorFlow & PyTorch
1
- 23.1
  Deep learning frameworks used to build, train, and deploy neural networks and advanced AI models.
XGBoost & LightGBM
1
- 24.1
  High-performance gradient boosting libraries for structured data and predictive modeling.
NLTK & SpaCy
1
- 25.1
  Natural Language Processing libraries for text preprocessing, tokenization, and linguistic analysis.
OpenCV
1
- 26.1
  Computer vision library for image processing, object detection, and video analysis.
Advanced SQL & Business Data Analytics
This module focuses on solving real-world business problems using advanced SQL techniques. Students apply Subqueries, CTEs, Window Functions, and CASE statements while working on industry-based case studies to build strong analytical and reporting skills.
0
Advanced SQL
3
- 28.1
  Multi-step queries with Subqueries and CTEs (Common Table Expressions)
- 28.2
  Performing time-based comparisons using Window Functions
- 28.3
  Applying conditional logic with CASE statements for flexible analysis
Projects
0
Walmart Sales SQL Analysis
1
- 30.1
  Query sales data to uncover store-wise and category-wise performance trends.
Fasos Orders Analysis
1
- 31.1
  Analyze food delivery orders to identify top-selling items and peak order times.
Zomato Orders SQL Report
1
- 32.1
  Generate reports on restaurant performance, customer preferences, and order volumes.
Data Cleaning with SQL Project
1
- 33.1
  Use SQL queries to clean, format, and prepare raw datasets for analysis.
OLA Project in SQL
1
- 34.1
  Explore ride-hailing data to track customer usage, driver performance, and trip trends.
Digital music store Analysis
1
- 35.1
  Analyze customer and revenue trends across countries, artists, and music genres.
Streamlit App Development
This module focuses on building interactive data applications using Streamlit, enabling rapid development of AI and data-driven web apps without complex frontend coding.
0
Streamlit Fundamentals
5
- 37.1
  Introduction to Streamlit Architecture
- 37.2
  Widgets: sliders, buttons, inputs, file uploads
- 37.3
  App Structure & Layout Design
- 37.4
  Displaying DataFrames, Charts, and Metrics
- 37.5
  Handling User Inputs & State Management
Data & Model Integration
5
- 38.1
  Integrating Pandas, NumPy & Visualization Libraries
- 38.2
  Connecting ML Models (Scikit-learn, TensorFlow, PyTorch)
- 38.3
  Real-time Predictions & Interactive Controls
- 38.4
  Performance Optimization Techniques
- 38.5
  Uploading & Processing External Data
Deployment & Scaling
5
- 39.1
  Streamlit Cloud & Local Deployment
- 39.2
  App Configuration & Secrets Management
- 39.3
  API Integration
- 39.4
  Authentication Basics
- 39.5
  Sharing & Hosting Data Apps
Projects
4
- 40.1
  Interactive Data Dashboard
- 40.2
  ML Prediction Web App
- 40.3
  AI-Powered Analytics Tool
- 40.4
  Real-Time Data Monitoring Application
Flask Web Development
This module focuses on building backend web applications using Flask, covering application structure, routing, database integration, authentication, and deployment.
0
Key Business Questions
3
- 42.1
  How can we build secure and scalable backend web applications using Flask?
- 42.2
  How can we integrate databases into web applications efficiently?
- 42.3
  How can we develop REST APIs for real-world business systems?
Flask Fundamentals
5
- 43.1
  Introduction to Flask & WSGI Concept
- 43.2
  Application Structure & Project Setup
- 43.3
  Routing & HTTP Methods (GET, POST)
- 43.4
  Working with Templates (Jinja2)
- 43.5
  Passing Data to Views
Database & Authentication
5
- 44.1
  Flask-SQLAlchemy Integration
- 44.2
  Models & ORM Basics
- 44.3
  CRUD Operations
- 44.4
  User Registration & Login System
- 44.5
  Session Management & Access Control
REST API & Deployment
5
- 45.1
  Building RESTful APIs
- 45.2
  JSON Responses & API Testing
- 45.3
  Error Handling
- 45.4
  Deploying Flask Applications
- 45.5
  Environment Configuration
Projects
4
- 46.1
  Blog Management System
- 46.2
  Task Management App (CRUD + Login)
- 46.3
  REST API for Product Management
- 46.4
  Authentication-Based Web Application
Introduction to Vibe Coding
Vibe Coding represents a shift from traditional coding toward idea-driven programming, where developers focus more on defining goals and logic while AI systems generate much of the implementation.Understand how modern developers use AI-assisted tools to build software faster through prompt-driven development and rapid iteration. This module introduces the foundations of Vibe Coding, AI pair programming, and human–AI collaborative development workflows used in modern software engineering.
0
Definition of Vibe Coding
1
- 48.1
  Vibe Coding refers to a development approach where programmers rely heavily on AI tools to generate, modify, and optimize code through natural language prompts rather than writing every line manually.
Human–AI Collaboration
1
- 49.1
  In this approach, developers act more like directors or architects, guiding AI models that produce the underlying code. The human defines the intention while AI produces the implementation.
Prompt-Driven Programming
1
- 50.1
  Developers describe desired functionality in natural language prompts, and AI generates working code based on that description.
Rapid Iteration Development
1
- 51.1
  Instead of spending long periods designing and coding manually, developers iterate quickly by modifying prompts and refining generated code.
Creativity-Focused Workflow
1
- 52.1
  Vibe coding allows developers to focus more on ideas, product design, and problem solving, while AI handles repetitive programming tasks.
AI-Powered Coding Workflows
0
Prompt → Code Generation
1
- 54.1
  Developers describe functionality in prompts, and AI generates scripts, functions, or full applications.
AI Pair Programming
1
- 55.1
  AI tools function like collaborative coding partners that assist with debugging, optimization, documentation, and code suggestions.
Code Autocompletion Systems
1
- 56.1
  Modern AI models predict and generate code blocks in real time as developers type, increasing productivity and reducing development time.
Code Explanation & Refactoring
1
- 57.1
  AI tools can analyze existing codebases and explain logic, restructure inefficient code, or optimize performance.
Testing & Debugging Assistance
1
- 58.1
  AI models can automatically generate unit tests, identify bugs, and recommend fixes.
Core Concepts Behind Vibe Coding
0
Natural Language Programming
1
- 60.1
  Developers communicate with AI systems using conversational language rather than traditional syntax-heavy programming.
Intent-Driven Development
1
- 61.1
  The developer specifies the intended functionality, and AI converts the intent into executable code.
Rapid Prototyping
1
- 62.1
  Vibe coding allows developers to quickly create prototypes for websites, applications, or automation tools.
Low-Barrier Entry to Programming
1
- 63.1
  AI assistance lowers the learning curve for beginners, enabling non-experts to build functional software with minimal coding knowledge.
Human Oversight & Verification
1
- 64.1
  Despite AI-generated code, developers must still verify logic, security, scalability, and system performance.
Advantages of Vibe Coding
5
- 66.1
  Accelerates development speed
- 66.2
  Helps beginners learn programming faster
- 66.3
  Reduces repetitive coding tasks
- 66.4
  Enables quick experimentation and prototyping
- 66.5
  Enhances developer productivity
Limitations and Risks
4
- 67.1
  AI-generated code may contain hidden bugs
- 67.2
  Security vulnerabilities can appear in generated code
- 67.3
  Understanding the underlying code remains essential for professional development
- 67.4
  Developers may become dependent on AI tools
Importance of Code Understanding
1
- 68.1
  Even in AI-assisted environments, developers must understand programming fundamentals to maintain quality and reliability.
Advantages and Limitations
5
- 69.1
  Web Application Development: Developers can generate front-end layouts, APIs, and backend logic quickly using AI coding assistants.
- 69.2
  Automation Scripts: AI tools help generate scripts for automating repetitive tasks in data processing, file management, or system operations.
- 69.3
  Rapid Startup Prototypes: Entrepreneurs can build minimum viable products (MVPs) quickly using AI-generated code.
- 69.4
  Data Science & Analytics Tools: Vibe coding helps data scientists write analysis scripts and visualization code efficiently.
- 69.5
  Educational Learning Platforms: Students use AI-assisted coding environments to learn programming through guided examples and explanations.
Tools
The following tools enable AI-assisted programming and Vibe Coding workflows.
6
- 70.1
  Cursor IDE : AI-native coding environment
- 70.2
  GitHub Copilot : AI-powered code completion and suggestions
- 70.3
  Replit AI : Cloud development with AI code generation
- 70.4
  Codeium : AI code autocomplete and search
- 70.5
  ChatGPT / LLM Coding Assistants : Prompt-driven code generation and explanation
- 70.6
  Tabnine : AI-powered code prediction system
Tools for Vibe Coding
1
- 71.1
  Vibe coding relies on AI-powered development tools that assist programmers in writing, understanding, and improving code through automation and intelligent suggestions. These tools act as AI coding assistants, helping developers generate code, autocomplete functions, debug programs, and accelerate the software development process.
Conceptual Workflow of Vibe Coding
6
- 72.1
  IDEA : The developer starts with a problem or concept for an application.
- 72.2
  Prompt to AI : The idea is explained to the AI using a clear instruction or prompt.
- 72.3
  Generated Code : The AI generates code that attempts to implement the requested functionality.
- 72.4
  Testing & Debugging : The developer runs the code to check for errors or unexpected behavior.
- 72.5
  Prompt Refinement : The prompt is improved to fix problems or add missing features.
- 72.6
  Improved Application : The AI produces better code after refinement, gradually forming a working application.
Key Idea
2
- 73.1
  Traditional programming focuses on writing code manually. Vibe Coding focuses on guiding AI to generate the code. The developer becomes less of a typist and more of a system designer who communicates intent and refines AI-generated solutions.
- 73.2
  Modern software development is increasingly moving toward hybrid intelligence, where human creativity and AI automation combine to accelerate innovation and software creation.
Introduction to Agentic AI
Agentic AI represents the next stage of artificial intelligence where systems can plan tasks, use tools, collaborate with other agents, and continuously improve outcomes. Understand how autonomous AI systems move beyond simple responses to goal-driven decision making and task execution. This module introduces the foundations of Agentic AI, intelligent agents, planning systems, and tool-using AI architectures used to build advanced automation systems capable of reasoning, acting, and adapting in real-world environments.
0
Agentic AI (Autonomous AI Systems)
0
Definition of Agentic AI
1
- 76.1
  Agentic AI refers to intelligent systems capable of independent decision-making, goal planning, and action execution without requiring constant human prompts. These systems operate as autonomous agents that observe environments, reason about tasks, and perform actions to achieve objectives.
AI Agents vs Traditional AI Systems
1
- 77.1
  Traditional AI systems respond only to instructions. Agentic AI systems initiate workflows, coordinate tasks, and dynamically adjust strategies based on results and feedback.
Core Components of AI Agents
1
- 78.1
  Agentic systems typically include perception modules, reasoning engines, planning algorithms, memory systems, and action modules that interact with external tools and environments.
Single-Agent vs Multi-Agent Systems
1
- 79.1
  Single-agent systems solve tasks independently, while multi-agent systems consist of multiple specialized agents collaborating, delegating subtasks, and sharing information to achieve complex objectives.
Agent Architecture & Decision Systems
0
Perception Layer
1
- 81.1
  The perception component collects information from various sources such as APIs, databases, sensors, documents, or internet resources. This data provides the contextual awareness required for decision-making.
Planning and Task Decomposition
1
- 82.1
  Agentic systems break complex objectives into smaller executable tasks using planning algorithms. Task decomposition allows the system to organize workflows logically and efficiently.
Reasoning and Decision Engines
1
- 83.1
  Agents use reasoning mechanisms such as logical inference, probabilistic models, and reinforcement learning to determine the best course of action.
Action Execution Layer
1
- 84.1
  The execution layer interacts with external systems to perform operations such as running scripts, querying databases, sending emails, controlling software tools, or triggering automation workflows.
Feedback and Iteration Loops
1
- 85.1
  Agentic AI continuously evaluates outcomes, learns from errors, and refines strategies. This iterative feedback loop improves performance over time.
Memory Systems & Context Management
0
Perception Layer
1
- 87.1
  The perception component collects information from various sources such as APIs, databases, sensors, documents, or internet resources. This data provides the contextual awareness required for decision-making.
Planning and Task Decomposition
1
- 88.1
  Agentic systems break complex objectives into smaller executable tasks using planning algorithms. Task decomposition allows the system to organize workflows logically and efficiently.
Reasoning and Decision Engines
1
- 89.1
  Agents use reasoning mechanisms such as logical inference, probabilistic models, and reinforcement learning to determine the best course of action.

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