Course Content

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Module 1: Introduction to Python

• What is Python and its role in AI/ML?

• Installing Python, Jupyter, and IDEs (VS Code, Anaconda)

• Writing your first Python program (Hello, World!)

• Understanding programming concepts and flow

Module 2: Variables and Data Types

• Basic data types: int, float, str, bool

• Variable declaration, naming conventions

• Type conversion and type checking

• Simple operations and string manipulation

Module 3: Control Structures

• Conditional logic: if, elif, else

• Logical operators: and, or, not

• Loops: for, while, and nested loops

• User input and basic validation

Module 4: Data Structures in Python

• Lists and list operations

• Dictionaries, sets, and tuples

• Iterating through data structures

• List comprehensions and basic error handling

Module 5: Functions and Modules

• Defining and calling functions

• Parameters, return values

• Scope and variable lifetime

• Importing built-in and custom modules

Module 6: File Handling & Exception Management

• Reading and writing files (text & CSV)

• Using with statements

• Exception handling: try, except, finally

• Common file I/O use cases in data tasks

Module 7: Object-Oriented Programming (OOP)

• Classes and objects

• Attributes, methods, and constructors

• Inheritance, encapsulation, and polymorphism

• Real-world examples (e.g., data models)

Module 8: Python Libraries for AI/ML

• NumPy for numerical computing

• Pandas for data manipulation

• Matplotlib & Seaborn for visualization

• Scikit-learn basics

Module 9: Data Handling and Preprocessing

• Loading datasets (CSV, Excel, web data)

• Handling missing values, duplicates

• Encoding categorical variables

• Feature scaling (Normalization & Standardization)