Letting Your IoT Customer Data Work For You
Self-driving cars, payment systems, wearables, cameras, IoT devices, smart industrial machines, and online tools. From software to hardware, intelligent services...
Are you just starting out with your first Machine Learning project and already asking yourself: “Which Python libraries are needed for Machine Learning?”. Getting started with the Python ecosystem and not getting lost on your way can be a daunting task, and we know it.
But we’re here to help and answer your question. We are going to take a look at the essential Python libraries for Machine Learning projects. For each of them, we’ll see which problem the library is meant to solve and how it can help you with various ML tasks. Also, we will try to focus on the libraries’ features that make them most useful in different stages of your ML project.
In this article, we cover Core Libraries and Data Modeling Libraries, since they represent the fundamental kit for any Machine Learning Python environment. In the second part, we will discuss Visualization Libraries.
The first obstacle you encounter when working with big data is the realization that numerical calculations with multidimensional arrays are slow and inefficient. The hero that first aids you against this hurdle is Numpy. Known as the fundamental package for working with multidimensional data, this library enables efficient computing in Python.
The beauty of Numpy lies in its simplicity. The library is defining N-dimensional array objects as the main data structure (allowing homogeneous data types to be stored). Basic operations on this data structure are then provided: indexing, reshaping, sorting & others.
Data engineers love these operations since they enable vectorization, which is an amazing feature: data processing tasks that would normally require loops can be defined by means of basic array expressions. This also means that numerical calculations are executed faster. Under the hood, these may even be distributed across multiple processing nodes for parallel computation.
Such advanced capabilities of handling large multidimensional arrays have turned Numpy into the core dependency for more high-level libraries.
Once you master the above data structures and become a master of computation with multidimensional arrays, you want to perform more advanced statistical analysis.
If we imagine Numpy to be the hero by your side, then we can say that Scipy is the hero’s extension pack. Scipy is using Numpy internally for manipulation of low-level data structures (multidimensional arrays) and provides submodules for statistical analysis.
Linear algebra algorithms, sparse matrix manipulation operations, clustering commands, and complex optimizations are all provided and already implemented. These heavy lifting submodules provide endless possibilities for developing error-prone scientific applications.
These two tools alone already make you ready for some serious scientific computation. But before you start doing that you take a look at your data...
Python Data Analysis Library pandas
Like most data engineers, you will very likely deal with unstructured, dirty or even misaligned data. It is sad, but it is true: your big data usually needs a lot of preprocessing and cleaning before being ready for the machine learning models.
Pandas is an open-source library that makes it easy to manipulate and analyze complex, multidimensional datasets. Really easy! By this point, it should come as no surprise to you that it is also built on top of Numpy and integrates seamlessly with Scipy.
Practically, the tool does not care if your data source is a CSV file, a SQL database or a JSON object. The extensive yet simple to use API, allows you to read data from any of these sources with just a method call. Pandas’ main data structures (Dataframes and Series) are optimized for complex manipulation tasks such as filtering and grouping. Last but not least, its plotting functionality allows quick analysis of trends and patterns in your data.
This vast functionality makes the Pandas library an essential tool in any data engineer’s toolbox, one that assists you in the most time-consuming stages of a machine learning project. Building ETL pipelines, preprocessing and cleaning data and doing exploratory data analysis have never been so fun before!
You’ve successfully cleaned and aligned your data thanks to the superpowers of the Core Libraries. The Exploratory Data Analysis already gave you a few ideas about which machine learning models you want to train on your data. But do you really need to implement all machine learning algorithms from scratch?
No worries, you don’t!
Scikit-learn is a comprehensive collection of both supervised and unsupervised algorithms. It provides a straightforward way to configure, train and evaluate different kinds of models with just a few lines of code. Regardless of the type of task that you are tackling, there are at least a handful of pre-configured models that are ready to be fitted and a bunch of matching performance measures ready for their evaluation.
From classification and regression to clustering and dimensionality reduction, Scikit-learn allows you to quickly prototype and deploy end-to-end learning pipelines. On top of that, the boosting algorithms and the ensemble methods will enable your model to achieve top-notch performance.
The powerful features of Scikit-learn allow you to quickly implement and integrate Machine Learning solutions into production systems. No wonder that this library has been the industry standard for such a long time!
The above list of libraries allows you to immediately get started with your Machine Learning project in Python. Did we miss any important library on our list? Let us know in the comment section below! Next time we will discuss Visualization Libraries and see what options there are to visualize the results of your data analysis.