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  • Welcome to UDSM Ai
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    • Exploring natural language processing
    • Building Blocks with numpy
    • Introduction to machine learning
  • 📚Resources
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      • 🖊️Introduction to AI (General summary)
      • 🖊️Views of AI
        • AI agents
        • Agents in different types of applications
      • 🖊️Knowledge representation in AI
        • Knowledge Representation
        • Formal Systems
        • Propositional Logic
        • Predicate Logic
          • Syntax
          • Semantics
      • 🖊️Approaches to Handle Uncertainty
    • Machine Learning
      • Introduction to Machine Learning
      • Fundamentals of Python Programming
      • Exploratory Data Analysis (EDA)
      • Supervised Learning Algorithms
        • Linear Regression
        • Logistic regression
  • 👷Hands On
    • Natural Language Processing
      • 📢Voice
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        • Text classification from scratch
          • Dataset Preparation
          • Text Representation
          • Tokenization
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          • Training
          • Evaluation
          • Inference
          • Fine-tuning and Optimization
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Fundamentals of Python Programming

Quick review on Python Programming Language

Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming.

Key Concepts:

  • Interpreted: Python code is executed line by line, making it easy to test and debug.

  • Dynamic Typing: Variables in Python are dynamically typed, meaning their types are inferred at runtime.

  • Indentation: Python uses indentation to define blocks of code, enhancing readability.

Data Types, Variables, and Operators

Data Types:

  • Numeric Types: Integers (int), Floating-Point Numbers (float), Complex Numbers (complex).

  • Sequence Types: Lists (list), Tuples (tuple), Strings (str).

  • Mapping Type: Dictionary (dict).

  • Boolean Type: Boolean (bool).

Variables:

  • Variables are used to store data values. They are created when a value is assigned to them.

  • Variable names must start with a letter or underscore (_), followed by letters, digits, or underscores.

Operators:

  • Arithmetic Operators: Addition (+), Subtraction (-), Multiplication (*), Division (/), Modulus (%), Exponentiation (**).

  • Comparison Operators: Equal to (==), Not equal to (!=), Greater than (>), Less than (<), Greater than or equal to (>=), Less than or equal to (<=).

  • Logical Operators: AND (and), OR (or), NOT (not).

Control Flow: Conditional Statements and Loops

Conditional Statements:

  • if Statement: Executes a block of code if a specified condition is true.

  • else Statement: Executes a block of code if the preceding if condition is false.

  • elif Statement: Adds additional conditions to evaluate if the preceding conditions are false.

Loops:

  • for Loop: Iterates over a sequence (e.g., list, tuple, string) or other iterable objects.

  • while Loop: Executes a block of code repeatedly as long as a specified condition is true.

  • Loop Control Statements: break (terminates the loop), continue (skips the current iteration), pass (placeholder, does nothing).

Functions and Modules

Functions:

  • Functions are reusable blocks of code that perform a specific task.

  • They improve code readability, organization, and modularity.

  • A function can accept parameters (inputs), perform operations, and return a result.

Modules:

  • Modules are files containing Python code, which can include functions, classes, and variables.

  • They allow code reuse and organization by separating related functionalities into different files.

  • Modules can be imported into other Python scripts using the import statement.

Introduction to NumPy and Pandas Libraries for Data Manipulation

NumPy:

  • NumPy (Numerical Python) is a powerful library for numerical computing in Python.

  • It provides support for multidimensional arrays (ndarrays) and various mathematical functions for array operations.

  • NumPy arrays are efficient and allow for fast vectorized operations, making them suitable for handling large datasets.

Pandas:

  • Pandas is a popular library for data manipulation and analysis built on top of NumPy.

  • It introduces two main data structures: Series (one-dimensional labeled arrays) and DataFrame (two-dimensional labeled data structures).

  • Pandas provides functionalities for data cleaning, filtering, aggregation, and visualization, making it essential for data preprocessing and exploration.

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Last updated 1 year ago

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