It’s kind of like learning how to speak a new language, except instead of saying “hello” or “bonjour,” you’re telling computers what to do with lines and lines of code. And when it comes to machine learning, AI, and data science, Python is the go-to language for all three because it has some pretty sweet libraries (which are like pre-made tools) that make things easier and more efficient.
For example, let’s say you want to build a model that can predict whether someone will buy a product or not based on their age, income, and other factors. With Python and its machine learning library called Scikit-Learn (which is like a toolbox for building models), you can do this in just a few lines of code:
# Load the dataset into memory
import pandas as pd
# Import the pandas library to work with dataframes
df = pd.read_csv('data/customer_data.csv')
# Use the read_csv function to load the dataset into a dataframe called df
# Split the data into training and testing sets
from sklearn.model_selection import train_test_split
# Import the train_test_split function from the sklearn library to split the data into training and testing sets
X, y = df[['age', 'income']], df['purchase']
# Assign the features (age and income) to the variable X and the target variable (purchase) to the variable y
X_train, X_test, y_train, y_test = train_test_split(X, y)
# Use the train_test_split function to split the data into training and testing sets for both the features and target variable
# Build a model using Scikit-Learn's LogisticRegression classifier (which is like a fancy calculator for making predictions based on data)
from sklearn.linear_model import LogisticRegression
# Import the LogisticRegression classifier from the sklearn library to build our model
clf = LogisticRegression()
# Assign the LogisticRegression classifier to the variable clf
# Train the model on the training set and evaluate its performance using Scikit-Learn's metrics module (which is like a scoreboard to see how well your model did)
clf.fit(X_train, y_train)
# Use the fit function to train the model on the training set
print('Accuracy:', clf.score(X_test, y_test))
# Use the score function to evaluate the performance of the model on the testing set and print the accuracy score
You now have a machine learning model that can predict whether someone will buy a product or not based on their age and income (assuming your data is accurate and representative of the population you’re trying to make predictions for).
Python for Machine Learning, AI, and Data Science in simpler terms: just a fancy way of saying that Python has some pretty sweet libraries for building models and making predictions based on data. And if you want to learn more about how to use these tools, check out our courses!