Introduction to Python with Google Colab

Introduction to Python with Google Colab

• Python is a high-level, interpreted programming language suitable for various applications.
• Google Colab is an online platform that allows users to write and execute Python code via Jupyter notebooks.

Google Colab Interface

• Kernels: The computational engine that executes code blocks.
• Cells: Containers for code or text, including markdown and code cells.
  • Key Shortcuts:
    • Shift + Enter: Execute the current cell and move to the next.
    • Shift + Tab: View documentation for the selected function.

Modules in Python

• A module is a file containing functions and definitions, similar to #include in C/C++.
• Import modules to access additional functions:
  • Recommended method: import module1 as mod1.

Basics of NumPy

• NumPy is essential for machine learning, enabling efficient operations with vectors, matrices, and arrays.

Creating Vectors

• Vectors can be created as one-dimensional arrays using NumPy:
  • Row Vector: vector_row = np.array([1, 2, 3])
  • Column Vector: vector_column = np.array([[1], [2], [3]])

Creating Matrices

• Use NumPy to create two-dimensional arrays (matrices).
• Larger matrices can illustrate advantages of sparse representations. For example, a sparse matrix minimizes storage of zero elements using sparse.csr_matrix().

Selecting Elements in Arrays

• NumPy arrays are zero-indexed.
• Selection example:
  • Third element of vector: vector[2]
  • Element at second row, second column in a matrix: matrix[1, 1]
• Various slicing techniques allow selection of groups of elements.

Describing Matrix Properties

• Use attributes shape, size, and ndim to describe a matrix:
  • Shape: matrix.shape indicates rows and columns.
  • Size: matrix.size gives total elements.
  • Dimensions: matrix.ndim returns the number of matrix dimensions.

Applying Functions to Arrays

• Functions can be applied to multiple elements using np.vectorize() or broadcasting techniques, e.g., matrix + 1000.

Finding Maximum and Minimum Values

• Use np.max() and np.min() to find max/min values in arrays.
• Operations can be specified along certain axes with parameters like axis=0 or axis=1.

Descriptive Statistics

• Calculate mean, variance, and standard deviation using np.mean(), np.var(), and np.std(), respectively.

Reshaping Arrays

• Reshape matrices while maintaining data with matrix.reshape(), ensuring the total number of elements remains unchanged.
• Use -1 to auto-calculate dimensions.

Introduction to Linear Algebra with NumPy

• Calculate eigenvalues and eigenvectors using np.linalg.eig().
• Compute matrix inverses with np.linalg.inv().

Post Lab Exercises

• Implement programs to:
  • Check if all elements in an array are non-zero.
  • Create a 2x2 matrix of ones with a border of zeros.
  • Multiply a randomly generated 4x3 matrix by a 3x5 matrix.
  • Solve a system of equations using np.linalg.solve().

This quiz covers the basics of Python programming using Google Colab. You'll learn about the interface, modules, and how to work with NumPy for efficient operations. Test your understanding of essential Python concepts and tools in this interactive quiz.