Starting Python with Machine Learning: 04 Classic ML with SciKitLearn
If you’re new to the world of machine learning and are looking to get started with Python, you’ve come to the right place. In this article, we’ll explore classic machine learning with the popular Python library, SciKitLearn.
What is SciKitLearn?
SciKitLearn, also known as sklearn, is a powerful library for machine learning in Python. It provides a wide range of algorithms for supervised and unsupervised learning, as well as tools for model evaluation and selection. It’s a great starting point for anyone looking to dive into the world of machine learning with Python.
Getting Started with Classic ML
Classic machine learning refers to traditional algorithms that have been around for a long time, such as linear regression, decision trees, and random forests. These algorithms are well-established and have proven to be effective in a wide range of applications. With SciKitLearn, you can easily implement these classic ML algorithms in Python.
Example: Linear Regression
Let’s take a look at a simple example using linear regression in SciKitLearn. First, you’ll need to install SciKitLearn if you haven’t already:
pip install scikit-learn
Once you have SciKitLearn installed, you can start by importing the necessary libraries and creating some sample data:
import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])
Next, you can create a linear regression model and fit it to the data:
model = LinearRegression()
model.fit(X, y)
Finally, you can use the model to make predictions:
predictions = model.predict([[5]])
print(predictions)
That’s it! You’ve just implemented a simple linear regression model using SciKitLearn in Python.
Next Steps
Once you’re comfortable with classic machine learning algorithms in SciKitLearn, you can start exploring more advanced techniques such as deep learning and neural networks. But for now, take some time to experiment with different algorithms and datasets to get a feel for how machine learning works in Python.
Happy coding!
It's an odd way to introduce Python… (an ML series), but I'd love it if you'd continue this and make it more ML and statistics-oriented though..