Introduction to Python Programming for AI & ML¶

Python is a powerful programming language widely used in the fields of Artificial Intelligence (AI) and Machine Learning (ML). This notebook serves as an introduction to Python, focusing on aspects that are particularly relevant for AI and ML applications.

Basics of Python¶

Printing and Variables¶

Python allows us to print text and store values in variables. Here's an example:

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message = "Welcome to AI & ML with Python!"
print(message)

message = "Welcome to AI & ML with Python!" print(message)

Data Types¶

Understanding basic data types such as integers, floats, strings, and booleans is essential.

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integer_number = 10
floating_number = 5.6
text = "Python for AI & ML"
flag = True

print(type(integer_number), type(floating_number), type(text), type(flag))

integer_number = 10 floating_number = 5.6 text = "Python for AI & ML" flag = True print(type(integer_number), type(floating_number), type(text), type(flag))

Lists and Dictionaries¶

Python provides powerful data structures like lists and dictionaries.

Lists¶

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my_list = [1, 2, 3, 4]
my_list.append(5)
print(my_list)

my_list = [1, 2, 3, 4] my_list.append(5) print(my_list)

Dictionaries¶

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my_dict = {'a': 1, 'b': 2, 'c': 3}
print(my_dict['b'])

my_dict = {'a': 1, 'b': 2, 'c': 3} print(my_dict['b'])

Control Structures¶

Control structures are fundamental in programming. They allow you to control the flow of the program.

Conditional Statements¶

The use of if, elif, and else to control the flow of code based on conditions.

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x = 10
if x > 10:
    print("x is greater than 10")
elif x == 10:
    print("x is equal to 10")
else:
    print("x is less than 10")

x = 10 if x > 10: print("x is greater than 10") elif x == 10: print("x is equal to 10") else: print("x is less than 10")

Loops¶

Loops are used to repeat code.

For Loops¶

In [ ]:

for i in range(5):
    print(i)

for i in range(5): print(i)

While Loops¶

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count = 0
while count < 5:
    print(count)
    count += 1

count = 0 while count < 5: print(count) count += 1

List Comprehensions¶

List comprehensions provide a concise way to create lists.

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squares = [x**2 for x in range(10)]
print(squares)

squares = [x**2 for x in range(10)] print(squares)

Lambda Functions¶

Lambda functions are small anonymous functions defined with the lambda keyword.

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multiply = lambda x, y: x * y
print(multiply(3, 4))

multiply = lambda x, y: x * y print(multiply(3, 4))

They are particularly useful when used with functions like map, filter, and reduce.

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numbers = [1, 2, 3, 4]
squares = map(lambda x: x**2, numbers)
print(list(squares))

numbers = [1, 2, 3, 4] squares = map(lambda x: x**2, numbers) print(list(squares))

Regular Expressions¶

Regular expressions are used to match patterns in strings. The re module in Python provides functionalities to work with regular expressions.

Matching a Pattern¶

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import re

pattern = re.compile(r'\d+') # Matches one or more digits
result = pattern.match("12345 is a number")
print(result.group())

import re pattern = re.compile(r'\d+') # Matches one or more digits result = pattern.match("12345 is a number") print(result.group())

Searching for a Pattern¶

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result = pattern.search("The number 12345 is here")
print(result.group())

result = pattern.search("The number 12345 is here") print(result.group())

Replacing a Pattern¶

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new_string = re.sub(r'\d+', 'NUMBER', "12345 is a number")
print(new_string)

new_string = re.sub(r'\d+', 'NUMBER', "12345 is a number") print(new_string)

Splitting a String Based on a Pattern¶

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split_string = re.split(r'\s+', "Split on spaces")
print(split_string)

split_string = re.split(r'\s+', "Split on spaces") print(split_string)

Regular expressions are powerful but can be complex. It's good to practice with various examples to get comfortable with them.

Conclusion¶

With the understanding of control structures, lambda functions, and regular expressions, you now have more tools to write effective and efficient Python code for AI and ML applications. These concepts form the building blocks of many algorithms and data processing tasks in AI and ML.

Conditional Statements¶

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x = 10
if x > 5:
    print("x is greater than 5")
else:
    print("x is not greater than 5")

x = 10 if x > 5: print("x is greater than 5") else: print("x is not greater than 5")

Loops¶

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for i in range(5):
    print(i)

for i in range(5): print(i)

Functions¶

Creating functions is essential for writing modular and maintainable code.

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def add_numbers(a, b):
    return a + b

print(add_numbers(3, 4))

def add_numbers(a, b): return a + b print(add_numbers(3, 4))

Libraries for AI & ML¶

Python’s ecosystem includes many libraries designed specifically for AI & ML.

NumPy¶

NumPy is a library for numerical operations.

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import numpy as np

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

import numpy as np array = np.array([1, 2, 3]) print(array)

Pandas¶

Pandas is used for data manipulation and analysis.

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import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 22]}
df = pd.DataFrame(data)
print(df)

import pandas as pd data = {'Name': ['Alice', 'Bob'], 'Age': [25, 22]} df = pd.DataFrame(data) print(df)

Scikit-learn¶

Scikit-learn provides tools for data mining and data analysis.

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from sklearn.datasets import load_iris

iris = load_iris()
print(iris.data[0:5])

from sklearn.datasets import load_iris iris = load_iris() print(iris.data[0:5])

TensorFlow and Keras¶

TensorFlow and Keras are popular for deep learning.

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import tensorflow as tf
from tensorflow import keras

print(tf.__version__)

import tensorflow as tf from tensorflow import keras print(tf.__version__)