Your First Steps into AI with Python and scikit-learn

Learn how to harness the power of scikit-learn, a leading machine learning library, within the collaborative environment of Google Colab. This tutorial provides a step-by-step guide for beginners, exp …

Updated August 26, 2023

Welcome to the exciting world of machine learning! In this tutorial, we’ll explore how to import and use scikit-learn (sklearn), a powerful Python library packed with tools for building intelligent models. We’ll be using Google Colab, a free online platform that provides all the necessary resources for running Python code and experimenting with machine learning.

What is scikit-learn?

Scikit-learn is like a toolbox filled with pre-built components for various machine learning tasks. Imagine you want to teach a computer to recognize handwritten digits, predict house prices, or classify emails as spam or not spam. Scikit-learn provides algorithms and tools to accomplish these goals and more!

Here’s a glimpse of what scikit-learn offers:

Classification: Algorithms for categorizing data into different classes (e.g., predicting if an email is spam or not).
Regression: Methods for predicting continuous values (e.g., forecasting house prices based on features like size and location).
Clustering: Techniques for grouping similar data points together (e.g., segmenting customers based on their purchase history).

Why Use Google Colab?

Google Colab is a fantastic platform for learning and experimenting with machine learning:

Free Access: You don’t need to install anything locally; just access it through your web browser!
Pre-Installed Libraries: Scikit-learn and other essential libraries are already installed, saving you setup time.
Collaboration: Share your notebooks and collaborate with others easily.
GPU Acceleration: For more demanding tasks, Colab provides access to powerful GPUs to speed up your model training.

Importing scikit-learn in Google Colab: A Step-by-Step Guide

Open a New Notebook: Go to https://colab.research.google.com/ and click “New notebook.”
Import the Library: In the first cell of your notebook, type the following code:

import sklearn

This line tells Python to load all the functions and tools within the scikit-learn library so you can use them in your code. Think of it like opening a toolbox and making its contents accessible.

Check the Version (Optional): To see which version of scikit-learn is installed, run:

print(sklearn.__version__)

Example: Using scikit-learn for Classification

Let’s try a simple example using scikit-learn’s LogisticRegression algorithm for classifying iris flowers.

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Load the iris dataset
iris = datasets.load_iris()
X = iris.data  # Features (petal length, width, sepal length, width)
y = iris.target # Labels (species of iris)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

# Create a Logistic Regression model
model = LogisticRegression()

# Train the model on the training data
model.fit(X_train, y_train)

# Make predictions on the test data
predictions = model.predict(X_test)

# Evaluate the accuracy of the model (optional)
accuracy = model.score(X_test, y_test)
print("Accuracy:", accuracy)

This example demonstrates a fundamental machine learning workflow: loading data, splitting it into training and testing sets, creating a model, training the model, making predictions, and evaluating performance.

Common Mistakes Beginners Make

Forgetting to Import: Always remember to import sklearn before using its functions.
Incorrect Function Names: Double-check spelling and capitalization when calling scikit-learn functions (e.g., LogisticRegression, not logisticregression).