Introduction to Machine Learning with Python

ML Musings
4 min readMar 11, 2023

Learn about the basics of Machine Learning and how to use Python libraries like scikit-learn to build machine learning models

Photo by Chris Ried on Unsplash

Machine learning is a rapidly growing field that allows computers to learn and make predictions without being explicitly programmed. It is a powerful tool for a wide range of applications, from image recognition and natural language processing to fraud detection and predictive analytics. Python is one of the most popular programming languages for machine learning, and has a rich ecosystem of libraries and tools that make it easy to build and deploy machine learning models.

In this article, we will provide an introduction to machine learning with Python and cover the basics of different machine learning algorithms and how to use Python libraries like scikit-learn to build machine learning models.

Let’s begin.

First, let’s take a look at the basic building blocks of machine learning. The three main components of machine learning are the model, the data, and the learning algorithm.

The model is the algorithm that you will use to make predictions. In scikit-learn, you can choose from a variety of pre-built models, such as linear regression, decision trees, and support vector machines.

The data is the input that you will use to train the model. In scikit-learn, you can load data from a variety of sources, including CSV files, SQL databases, and NumPy arrays.

The learning algorithm is the method that the model uses to learn from the data. The most common learning algorithms are supervised and unsupervised learning. Supervised learning is used when the output is known, and unsupervised learning is used when the output is unknown. Scikit-learn supports both supervised and unsupervised learning.

Now that we have a basic understanding of the components of machine learning, let’s build a simple machine learning model using scikit-learn. For this example, we will use the Iris dataset, which is a dataset of flower measurements that is commonly used for machine learning.

Here’s an example of how to load the Iris dataset and train a simple model using scikit-learn:

from sklearn.datasets import load_iris



ML Musings

✨ I enjoy pushing the boundaries of JS, Python, SwiftUI and AI. You can support my work through coffee -