AI/ML with Python Documentation

Day 1: Python Basics for AI/ML

1

Python Fundamentals

On the first day, we learned the basics of Python programming which form the foundation for AI and Machine Learning.

Key Python Concepts:

Variables and Data Types (integers, floats, strings, booleans)
Data Structures (lists, tuples, dictionaries, sets)
Control Flow (if statements, for and while loops)
Functions and Lambda expressions
File I/O operations
Error handling with try-except blocks
List comprehensions

Example Python Code:

                        # Basic Python operations

                        # List comprehension example

                        numbers = [1, 2, 3, 4, 5]

                        squares = [x**2 for x in numbers]

                        print(squares)  # Output: [1, 4, 9, 16, 25]

                        # Dictionary example

                        student = {

                            "name": "John",

                            "age": 21,

                            "major": "Data Science"

                        }

                        print(student["name"])  # Output: John

Python Syntax

Clean and readable syntax with significant whitespace.

Data Structures

Powerful built-in data structures for efficient data handling.

Functions

First-class functions and support for functional programming.

Day 2: NumPy Library

2

Numerical Computing with NumPy

On the second day, we explored NumPy, the fundamental package for scientific computing in Python.

NumPy Key Features:

Powerful N-dimensional array object
Sophisticated broadcasting functions
Tools for integrating C/C++ and Fortran code
Linear algebra, Fourier transform, and random number capabilities

NumPy Code Examples:

                        # Importing numpy

                        import numpy as np

                        # Creating arrays

                        arr1 = np.array([1, 2, 3, 4, 5])

                        arr2 = np.zeros((3, 3))  # 3x3 array of zeros

                        arr3 = np.ones((2, 4))   # 2x4 array of ones

                        # Array operations

                        a = np.array([1, 2, 3])

                        b = np.array([4, 5, 6])

                        print(a + b)  # Output: [5 7 9]

                        print(a * b)  # Output: [4 10 18]

                        # Reshaping arrays

                        arr = np.arange(12)

                        reshaped = arr.reshape(3, 4)

                        print(reshaped)

ND Arrays

Efficient multi-dimensional arrays for numerical data.

Broadcasting

Powerful broadcasting capabilities for array operations.

Random Module

Comprehensive random number generation for simulations.

Day 3: Matplotlib Library

3

Data Visualization with Matplotlib

On the third day, we learned about Matplotlib, a comprehensive library for creating static, animated, and interactive visualizations.

Matplotlib Key Features:

Line plots, scatter plots, bar charts, histograms
Pie charts, stem plots, contour plots
Customizable labels, titles, and legends
Multiple subplots in one figure
Save figures in various formats (PNG, PDF, SVG, etc.)

Matplotlib Code Examples:

                        # Importing matplotlib

                        import matplotlib.pyplot as plt

                        import numpy as np

                        # Simple line plot

                        x = np.linspace(0, 10, 100)

                        y = np.sin(x)

                        plt.plot(x, y)

                        plt.title('Sine Wave')

                        plt.xlabel('x')

                        plt.ylabel('sin(x)')

                        plt.show()

                        # Scatter plot

                        x = np.random.randn(100)

                        y = np.random.randn(100)

                        plt.scatter(x, y)

                        plt.title('Scatter Plot')

                        plt.show()

Line Plots

Create detailed line plots with customization options.

Scatter Plots

Visualize relationships between variables with scatter plots.

Bar Charts

Compare categorical data with various bar chart styles.

Day 4: Seaborn Library

4

Statistical Data Visualization with Seaborn

On the fourth day, we explored Seaborn, a Python data visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics.

Seaborn Key Features:

Built-in themes for styling Matplotlib graphics
Visualization of univariate and bivariate distributions
Plotting statistical time series data
Tools for fitting and visualizing linear regression models
Functions for visualizing matrices and using color palettes

Seaborn Code Examples:

                        # Importing seaborn

                        import seaborn as sns

                        import matplotlib.pyplot as plt

                        # Load example dataset

                        tips = sns.load_dataset("tips")

                        # Create a violin plot

                        sns.violinplot(x="day", y="total_bill", data=tips)

                        plt.show()

                        # Create a heatmap

                        import numpy as np

                        uniform_data = np.random.rand(10, 12)

                        ax = sns.heatmap(uniform_data)

                        plt.show()

                        # Pair plot for multivariate analysis

                        iris = sns.load_dataset("iris")

                        sns.pairplot(iris, hue="species")

                        plt.show()

Statistical Plots

Specialized plots for statistical analysis like violin plots.

Heatmaps

Visualize matrix-like data with color-coded heatmaps.

Pair Plots

Explore relationships between multiple variables simultaneously.

Practice & Assignments

Hands-on Exercises

After learning the essential libraries, we practiced with various exercises and assignments to solidify our understanding.

Practice Exercises:

Exercise 1: NumPy Array Operations

Create a 5x5 array with random values and find the minimum and maximum values. Then normalize the array so all values fall between 0 and 1.

Exercise 2: Matplotlib Visualization

Create a line plot of the sine and cosine functions from 0 to 2π. Add a legend, title, and customize the line styles and colors.

Exercise 3: Seaborn Statistical Plot

Load the built-in "tips" dataset in Seaborn and create a visualization that shows the relationship between total bill and tip amount, differentiated by time of day (lunch vs dinner).

Exercise 4: Comprehensive Project

Create a Jupyter notebook that demonstrates data loading, cleaning with Pandas, analysis with NumPy, and visualization with both Matplotlib and Seaborn using a real-world dataset of your choice.

Learning Outcomes:

Ability to manipulate numerical data efficiently with NumPy
Skills to create various types of plots with Matplotlib
Understanding of statistical visualization with Seaborn
Experience with real-world data analysis workflows
Preparation for more advanced machine learning concepts

Additional Resources

Further Learning

Expand your AI/ML knowledge with these recommended resources.

Recommended Books

• "Python for Data Analysis" by Wes McKinney
• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
• "Python Data Science Handbook" by Jake VanderPlas

Online Resources

• NumPy Official Documentation
• Matplotlib Gallery
• Seaborn Tutorials
• Kaggle Learn Courses

Practice Platforms

• Kaggle Notebooks
• Google Colab
• Jupyter Notebook
• DataCamp Exercises

AI/ML with Python Module

Mentor:

Shrideep Dhore , Hridaya Amle & Prathamesh Ingle