Python is one of the most commonly used programming languages by data scientists and machine learning engineers. Read out the content on how to build machine learning algorithms in Python.
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 Python machine learning algorithms. Python outranked other languages commonly used in the data science community including R, Scala, and Julia.
In this blog, The machine-learning-related Python packages and libraries you should know are introduced . In addition, let's discuss how to implement machine learning algorithms using the Python language.
Why is Python good for machine learning?
Full SDLC
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). Therefore, machine learning using Python just makes sense.
Relevant data science and machine learning libraries
Python has one of the largest collection of machine learning libraries (let's go into them more a bit later). These libraries remove the tedious work of coding entire algorithms from scratch and can easily integrate into your machine learning framework.
Community support
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.
Should I learn Python before machine learning?
Absolutely. Understanding Python fundamentals, Python data structures, and other Python concepts will allow you to fully immerse in an SDLC — advantageous for creating efficient applications. Additionally, machine learning using Python relies on reputable libraries that will help you out as well.
Python machine learning libraries
Before we jump into discussing specific algorithms, you need to learn more about relevant machine learning libraries.
Here are some of the most popular python machine learning libraries:
Scikit-learn
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.
NumPy
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.
Theano
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
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.
Pandas
A data scientist’s work begins with cleaning data, organizing data, and exploratory analysis. Pandas is a Python 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
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
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 – How Python is used in machine learning
Now let’s get into some machine learning algorithms in python.
Linear regression
Linear Regression is one of the most basic and powerful machine learning algorithms in Python 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 machine learning libraries: Scikit-learn, Matplotlib, Pandas, NumPy, PyTorch
Logistic regression
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 machine learning libraries: Scikit-learn, Matplotlib, NumPy.
Decision Tree
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 machine learning libraries: Scikit-learn, Pandas
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 machine learning libraries: NumPy, Scikit-learn
K-Means Clustering
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. Instead, 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 machine learning 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 machine learning libraries: NumPy, Scikit-learn, Keras
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