Nowadays, the data revolution is breaking all boundaries and has brought about a tsunami of new information. And with so many resources to be used for a machine learning project, knowing where to find examples and algorithms to use can be a challenge.
Therefore, if you want to get started with Python machine learning without going through tons of research you have some options at your disposal. In this post, I will highlight some of the best machine learning libraries that you don't want to miss when researching new projects.
What is machine learning?
In machine learning, the goal is to teach a computer how to figure out a set of rules or a process that makes it able to do something.
For example, if you want your email server to be able to identify spam before you should start scanning all your mailboxes, first, first identify what it means for an email to be spam.
It is the computer's job to use machine learning algorithms and predictive analytics where it will learn from its mistakes and can make instant decisions about what email is spam based on well-defined patterns.
What can different machine learning libraries do?
A machine learning library is an application programming interface (API) that allows users to create computer programs with specific purposes. Today, no matter what type of machine learning you're doing, there are different libraries available.
Here is the list of top machine learning libraries that can be useful to you to start with machine learning.
Scikit Learn, with its very clear API and easy-toes summary, is designed to make machine learning tasks simple. It is cross-platform so you can use the same code with your Python 3 or Python 2 applications with no changes.
It also provides different algorithms, all in a single package so you don't have to worry about building packages, managing dependencies, or cleaning data with other utilities.
Scikit learn is a set of python libraries primarily aimed at machine learning tasks. In order to perform machine learning tasks, you will need learners, proper data collection tools, and a platform for training these models.
Scikit learn can be used for this purpose with features that include: help waypoints, model selection, feature extraction, and graphing.
NumPy is one of the most popular machine learning libraries that is used in Python. This library allows users to perform complex vector operations quickly and efficiently.
It can be difficult to find shortcuts when working with NumPy matrices, but this tutorial covers all of the tricks.NumPy is a library constructed for scientific computing with Python.
NumPy makes excellent use of the high-performance NVIDIA GPU. For many, NumPy provides a way to accurately encode and process data arrays along with efficient linear algebra capabilities. In short, NumPy is a key foundational tool that every machine learning developer should have in their toolbox.
TensorFlow is without a doubt one of the more advanced machine learning libraries available. It is capable of performing complex computation for your data, and on some platforms, it can be deployed as a service with Google having elected to make it public for such uses.
TensorFlow is currently the most popular library for machine learning in Python. It's also widely used for research purposes because it's so flexible and powerful.
It's an open-source library that is developed by Google researchers. The introduction of TensorFlow was to give machine learning researchers a reliable, scalable way to work with current generation neural network architectures. It has been used by DeepMind, Microsoft, and others to produce original machine learning research findings.
SciPy is a powerful Python library for scientific computing. It has many advanced features for implementing machine learning algorithms. Low-level linear algebra, statistics, optimization, signal processing, and others are all implemented using the SciPy package.
This is especially great because your code can be easily ported to other languages if you need it to be. SciPy provides with it the most comprehensive set of machine learning algorithms. This open-source library has programmers in mind.
One of the most popular Python machine learning libraries out there is Keras. Keras is a library that you can use to take a complicated task and break it down into small independent modules that belong together.
Keras also provides a number of other powerful features, such as allowing your model to learn from example images. One of the top libraries in the Python machine learning space is Keras.
This library reduces the amount of time that it takes to train models considerably. It also allows for neural networks with hundreds of millions of parameters, while still staying at a lower abstraction layer, meaning you can switch back and forth while training the model.
Pandas is one of the most popular Python machine learning libraries. If you ever need to go deeper into your data, this library will come in handy because it gives users an easier way to manipulate and analyze data.
Some of the useful features of Pandas are coordinate arithmetic, easy analytical models implementation, versatile indexing, and powerful joins.
The most basic of machine learning libraries, Pandas is a replacement for Matrices and NumPy arrays. This has numerous performance boosts which makes it a good choice to use in machine learning projects. While it may take some time to get used to using Pandas, the effort will pay off in the end.
Matplolib offers visualization capabilities similar to other libraries in the Python scientific ecosystem. Historically, it has been difficult to use Matplotlib interactively.
However, the new notebook installed with Anaconda, a popular free Python distribution introducing a new interactive web-based platform for Python, allows users to create executable code and manipulate plots on a browser-based interface.
Matplotlib compiles matrices into bitmaps or n-tuples for rendering in png, jpeg, postscript, pdf, svg, eps files. The interactive tool permits plotting functions that calculate their output multiple times depending on the input arguments. This is useful for taking images of arbitrary length along with x and y coordinates specifying its boundaries.