Machine Learning with Python

Understand the basics of machine learning and explore how Python's Scikit-learn library can be used for predictive modeling.

By Kevin McAleer,    3 Minutes

Introduction

Machine Learning is a method of data analysis that automates analytical model building. Python, with its strong set of libraries like Scikit-learn, makes it a great language for Machine Learning. This lesson will introduce the basics of machine learning and how to use the Scikit-learn library for predictive modeling.

Learning Objectives

• Understand what machine learning is and the different types of machine learning.
• Learn how to use the Scikit-learn library for machine learning.

What is Machine Learning?

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Types of Machine Learning

There are three major types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

• Supervised Learning: The algorithm learns from labeled data. Given a set of training examples, the algorithm tries to find the best model to relate the inputs to the output.

• Unsupervised Learning: The algorithm learns from unlabeled data and finds hidden patterns or intrinsic structures in the input data.

• Reinforcement Learning: The algorithm learns to perform an action from experience. It is trained to make specific decisions by rewarding and punishing behaviors.

Introduction to Scikit-learn

Scikit-learn is one of the most popular open-source machine learning libraries for Python. It provides a range of supervised and unsupervised learning algorithms.

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm

# Load dataset
iris = datasets.load_iris()

# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)

# Create a model
clf = svm.SVC(kernel='linear', C=1)

# Train the model using the training sets
clf.fit(X_train, y_train)

# Predict the response for test dataset
y_pred = clf.predict(X_test)

# Print the predictions
print(y_pred)

Summary

In this lesson, you’ve learned about the basics of machine learning, including an understanding of different types of machine learning. We also introduced the Scikit-learn library and demonstrated how it can be used for predictive modeling. Machine learning is a powerful tool for data analysis and predictive modeling.