Python is one of the most commonly used programming languages by data scientists and machine learning engineers. Although there has been no universal study on the prevalence of machine learning algorithms in Python in machine learning, a 2019 GitHub analysis of public repositories tagged as “machine-learning” not surprisingly found that Python was the most common language used.
Python outranked other languages commonly used in the data science community including R, Scala, and Julia.
This is all to say that if you’re interested in being a data scientist or a machine learning engineer, then understanding Python should be on your to-do list. It’s important to remember though that employing machine learning techniques involves more than just coding for coding’s sake. An important part of the job of a data scientist or machine learning engineer is using programming languages to apply statistical methods and develop machine learning algorithms.
In this blog we’ll introduce you to machine-learning-related Python packages and libraries you should know. In addition, we’ll discuss how to implement machine learning algorithms using the Python language.
Why is Python so popular for machine learning?
Here are a few reasons why Python has become the go-to programming language for machine learning:
Python is increasingly becoming the main programming language taught in introductory computer science courses in high schools and universities. It’s easy-to-learn syntax and structure also makes it a great introductory language for self-taught programmers. This simplicity also extends into the workplace as Python’s readability is helpful for data scientists who want to explain their process to non-technical colleagues.
Unlike R, which is fundamentally a statistical programming language or SQL, which is meant for querying databases, Python is a language that can be used to build full applications. For engineers who intend to create applications based on machine learning algorithms, it is advantageous to be able to use a language that can be used throughout the entire software development lifecycle (SDLC).
Relevant data science and machine learning libraries
Python has one of the largest collections of machine learning libraries (we’ll go into them more a bit later). These libraries remove the tedious work of coding entire algorithms from scratch.
Python is an open-source language with an active developer community. Because of this, it is easy for developers to find information through regularly updated documentation or online forums.
Python’s machine learning libraries
Before we jump into discussing specific algorithms, you need to learn more about relevant machine learning libraries. Please note, we will be naming some algorithms here, but will go into more detail about their use in the next section.
Here are some of the most popular python machine learning libraries:
You can’t have a discussion about Python machine learning libraries without first mentioning Scikit-learn. It’s a broad library that contains most classical machine learning methods, including supervised and unsupervised learning techniques. According to the GitHub report referenced earlier, over 40 percent of machine learning projects in the repository use the Scikit-learn library.
Check out our how-to guide on deploying a model from Scikit-learn to Algorithmia.
NumPy is a package designed for high-level and complex mathematical functions, particularly linear algebra. It is commonly used for machine learning projects like image processing.
Similar to NumPy, Theano is a scientific computing library. It differs from NumPy in that it optimizes CPU utilization meaning that it can complete calculations up to 100-times faster than other methods. This performance speed has made Theano a popular library for developing deep learning AI applications.
PyTorch is Python’s version of Torch, a machine learning library for the C programming language. It is particularly useful for machine learning tasks like natural language processing.
Our blog post on convolutional neural networks in PyTorch is a great place to learn more about this framework.
A data scientist’s work begins with cleaning data, organizing data, and exploratory analysis. Pandas is a library for these types of tasks. So while it is not a machine learning tool in and of itself, you really can’t start writing and testing algorithms without it or something like it. Pandas is also helpful in that it can read data from relational and nonrelational databases.
Matplotlib is a data visualization library. While data visualization is not machine learning, creating charts and graphs is necessary for the exploratory analysis phase of data science. In addition, if you are planning on presenting your work to non-technical people, you will need to make use of a visualization library.
Keras a neural network library. Although it is relatively new in relation to other Python libraries, it has gained popularity because of its user friendliness and facilitation of fast prototyping.
NLTK (Natural Language Toolkit)
NLTK is actually a collection of python libraries and modules to support natural language processing. The platform provides libraries for tasks like analyzing language structure and categorizing text.
Python machine learning algorithms
Now let’s get into some machine learning algorithms. In this section we’ll explain the purpose of some of the most commonly used algorithms and point out which Python libraries are the most useful in developing them.
Linear regression is one of the most basic and powerful algorithms that a data scientist can use. Its purpose is to predict a numeric target variable based on one or more independent variables. For example, a linear regression model could help you predict the price of a house if you were given variables like square footage, number of rooms, proximity to a police station, etc.
Relevant Python libraries: Scikit-learn, Matplotlib, Pandas, NumPy, PyTorch
You can check out how to develop a linear regression model in Python here.
Logistic regression is somewhat of a misnomer, as it’s actually a classification technique used to estimate the probability of a new observation belonging to a particular category. Class probability estimation can be used in churn models when you want more nuance about a customer’s likelihood of leaving. It could help a business team develop more focused plans for different types of customers.
Relevant Python libraries: Scikit-learn, Matplotlib, NumPy
Scikit-learn’s documentation can walk you through how to develop a logistic regression model in Python here.
Decision tree is another classification method that takes the visual form of an upside down tree-like structure. Each branch of the tree represents a decision point, and the leaf is the outcome. Its structure makes it easy for non-technical people to understand. A decision tree algorithm could be used for a task like deciding whether or not an applicant qualifies for a loan based on a set of attributes.
Relevant Python libraries: Scikit-learn, Pandas
You can find a decision tree example in Python here.
K-Nearest Neighbor (KNN)
Nearest neighbor models can be used for classification or regression. You predict the numerical value or class of a new observation by looking at its closest “neighbors”–the existing points in the data set. In a classification situation, the new observation falls into the class of the majority of the neighbors. In regression, the new observation is the average of the neighbors’ values. “K” is simply the number of neighbors you choose in your model.
Relevant Python libraries: NumPy, Scikit-learn
Clustering algorithms share some traits with nearest neighbors algorithms in that similarity and distance are important. Clustering algorithms, however, are a form of unsupervised learning meaning that there is no target variable. Insead, you are looking for patterns in a data set. The purpose of a K-means algorithm is to group similar observations around a central point. This algorithm is commonly used in marketing to uncover new segments and develop ways to target them based on their shared characteristics.
Relevant Python libraries: Pandas, NumPy, Scikit-learn, Matplotlib
Principal Component Analysis (PCA)
PCA is a dimension reduction method. The goal of these techniques is to take a large and sometimes unmanageable dataset and turn it into something smaller and much easier to analyze. In the case of PCA, you are reducing the dataset by eliminating redundant and correlated variables, and producing a new data set of uncorrelated variables known as principal components. PCA is sometimes used to summarize demographic or behavioral data from large surveys.
Relevant Python libraries: NumPy, Scikit-learn, Keras
How can I learn more about machine learning algorithms in Python?
You may have noticed that we linked to the documentation from some Python libraries and the tutorials from the Data Camp community. We think that these are great step-by-step guides to help you navigate the steps of developing machine learning models.
In addition, we recommend looking at code from Kaggle competitions. If you’re unfamiliar with Kaggle, it is a site where members compete to solve machine learning and data science problems. What’s particularly useful about the site is that you can see how multiple people approached the same problem.
And take a look at “machine-learning” tagged projects on GitHub. In addition to learning more about Python, you’ll also learn how other programming languages are used for machine learning.
Finally, check out the more than 8,000 algorithms we host on our website and run any of them in Python.