During the early days of Machine Learning – when it was not yet a mainstream technology – Developers had to perform Machine Learning tasks by manually coding each ML algorithm using mathematical and statistical formulas. Naturally, the process was both time and labour-intensive. Thankfully, we don’t have to do this anymore!
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Ever since Machine Learning entered the mainstream tech domain, the ML community has been evolving at an unprecedented pace. As a result, today, we have an exhaustive inventory Machine Learning libraries and Machine Learning frameworks at our disposal.
Essentially, Machine Learning libraries refer to sets of functions and routines written in a specific programming language. These libraries make the task of ML Developers/ML Engineers much easier by allowing them to perform complex tasks without having to rewrite endless lines of code.
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In this post, we’ll talk about some of the most popular and widely used Machine Learning libraries.
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TensorFlow is extensively used for training and deploying models on Node.js as well as in browsers. While you can use the core library to develop and train ML models in browsers, you can use TensorFlow Lite (a lightweight library) to deploy models on mobile and embedded devices. If you wish to train, validate, and deploy ML models in large production environments, TensorFlow Extended is there to help you.
NumPy is a Python-based Machine Learning library for scientific computing. It includes sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, and a powerful N-dimensional array object. NumPy is extensively used for large multi-dimensional array and matrix processing by using high-level mathematical functions. Apart from this, it is excellent for linear algebra, Fourier transform, and random number capabilities.
You can use NumPy as an efficient multi-dimensional container of generic data wherein arbitrary data-types can be defined. This further encourages seamless and speedy integration with many different databases.
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SciPy is a Python-based ML ecosystem for mathematics, science, and engineering. It is primarily used for scientific and technical computing. SciPy builds on the NumPy array object. It is a component of the NumPy stack including tools like Matplotlib, Pandas, SymPy, and a host of other scientific computing libraries. The underlying data structure leveraged by SciPy is a multi-dimensional array offered by the NumPy module.
SciPy contains modules for some of the commonly performed tasks in scientific programming such as optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ordinary differential equation solving, ad much more.
Scikit-Learn is an open-source Python-based Machine Learning library that is built on three other Python Libraries – NumPy, SciPy, and Matplotlib. Scikit-Learn packs in a host of ML algorithms including classification, regression, clustering and dimensionality reduction, Naive Bayes, Gradient boosting, K-means, model selection, to name a few. It is an excellent tool for data-mining, data analysis, and statistical modelling.
One of the best features of Scikit-learn is that it has excellent documentation along with a huge support community. Its only drawback is that it does not support distributed computing for large scale production environment applications.
Another Python-based Machine Learning library on our list, Theano is quite similar to NumPy. It can take structures and convert them into efficient code that uses NumPy and other native libraries. Theano is mainly used for numerical computation. It can handle different types of computation required for large neural network algorithms used in Deep Learning.
Theano lets you define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It has neat symbolic differentiation and allows for dynamic code generation in C. Perhaps the greatest aspect of this ML library is that it takes advantage of GPU which makes data-intensive calculations up to 100 times faster than when it runs on CPU alone. Theano’s speed is what makes it a potent tool for complex computation tasks and Deep Learning projects.
PyTorch is one of the open-source Deep Learning libraries that drew inspiration from the Torch library. It was developed by Facebook’s AI research team, and as the name indicates, it is a Python-based library. While it has a C++ frontend, it features a highly polished Python interface.
PyTorch is mainly used for natural language processing and computer vision applications. The “torch.distributed” backend of PyTorch enables scalable distributed training and performance optimization both in research and production. The two core features of PyTorch are Deep Neural Networks (based on a tape-based auto diff system) and Tensor computing using GPUs.
Keras is an open-source neural network library written in Python. It can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML. Since Keras was designed to facilitate fast experimentation with Deep Neural Networks, it is highly user-friendly, modular, and extensible. While Keras can very well handle rapid experimentation with Deep Neural Nets, it cannot support low-level computation so well – it uses the “backend” library for this purpose.
The biggest advantage of Keras is speed. It has built-in support for data parallelism and hence, it can process large volumes of data while simultaneously speeding up the time needed to train models.
Pandas is one of the best open-source data manipulation and data analysis libraries available today. It is based on NumPy that contributes numerous useful functions for accessing, indexing, merging, and grouping data. In fact, Pandas can be considered as the Python equivalent of Microsoft Excel – when it comes to any kind of tabular data, you must consider Pandas.
Pandas was developed explicitly for data extraction and preparation. So, while it may not be directly related to ML, it comes in handy for data preparation before training ML models. It has many high-level data structures and a wide variety of tools for data analysis along with inbuilt methods for groping, combining and filtering data. Pandas allows you to perform standard operations by writing only a few lines of code. For complex tasks, there are many Pandas commands that can help to make your code concise and neat.
Matpoltlib is one of the most important data visualization library written in Python. It is a 2D-plotting library that can be used to create 2D-graphs and plots. Just like Pandas, it is not directly related to Machine Learning. However, it is a powerful visualization tool that helps in visualizing patterns in large datasets.
Matplotlib has an object-oriented API for embedding plots into applications using general-purpose GUI toolkits ( for example, Tkinter, wxPython, Qt, and GTK+). It also contains the PyPlot module that makes the plotting process easier by offering features to control line styles, font properties, formatting axes, and so on. With Matplotlib, you can create plots, bar charts, histograms, power spectra, error charts, scatterplots, and much more.
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These are 9 of the best Machine Learning libraries you can get your hands on! The Machine Learning libraries we’ve mentioned here should take care of almost every ML need and requirement.
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How long does it take to learn Machine Learning?
Machine learning is a highly specialized field and one of the most rewarding career options in today's times. A subspecialty of Artificial Intelligence, machine learning is a vast subject that involves statistics, mathematics and programming computations, and data science, among other aspects. So, how long you might take to learn this subject will primarily depend on your present level of expertise in areas like programming, data modeling, and mining. Still, to consider a timeline, it can take you from 3 months up to 6 years or even more to learn machine learning.
Can non-technical people learn Python?
Python has quickly emerged as one of the most popular computer languages of all times. It's easy-to-read and neat syntax make this programming language easy to learn by even those who have no technical background or experience. The constructs of Python have attracted many people who are keen to learn data science, data analytics and BI, but hail from educational backgrounds like commerce or finance. They can now easily learn Python and pursue careers in emerging fields like data analytics, full-stack development, and business analysis. While it might not be the so-called perfect programming language, it is rich with many features and is simple enough to gain rapid popularity worldwide among both technical and non-technical professionals.
What jobs can you get after learning Machine Learning?
As the field of machine learning keeps evolving and expanding, it keeps opening up new opportunities to those who aspire to build a career out of it. As a matter of fact, machine learning is a promising and lucrative path of career, to say the least. Students and aspirants can look forward to exciting learning experiences as well as high-paying jobs too. Some of the most rewarding jobs in the machine learning career path are – machine learning engineer, data scientist, computational linguist, and human-centered machine learning designer.
Why are libraries used in Machine Learning?
Machine Learning libraries are a collection of functions and procedures that are ready to use. Learning to deal with libraries is vital regardless of the programming language or the domain in which a developer works since it simplifies things and reduces tiresome labor. A solid selection of libraries is an essential aspect of a developer's toolkit for researching and building sophisticated applications without having to write a lot of code. The formation of repetitive code can be avoided by using libraries. There are other libraries also that are dedicated to various topics, such as text processing libraries, graphics libraries, data manipulation libraries, and scientific calculation libraries, etc. Hundreds of Machine Learning libraries are under active development as Machine Learning continues to open up new possibilities for mankind and attract newcomers.
What are the functions in NumPy and SciPy libraries?
The two most significant Python libraries are NumPy and SciPy. Both libraries contain a large number of functions. SciPy stands for Scientific Python, whereas NumPy stands for Numerical Python. Python is used to write both of their functions. For homogeneous array operations, we utilize the NumPy library NumPy is used to manipulate elements of numerical array data. As a result, NumPy extends Python's capability and serves as a user-friendly replacement. The most significant scientific Python library is SciPy. It has a collection of functions since it is made up of several sub-packages. Clustering, image processing, integration, and other operations are supported by the packages. It's a pretty consistent library. Therefore it's great for numerical Python computations.
What is GUI?
The GUI or graphical user interface is a user interface that uses icons, menus, and other visual indications or depictions to allow users to interact with electronic devices such as computers and smartphones. Unlike text-based interfaces, where data and commands are purely in text, GUIs graphically show information and corresponding user controls. A pointing device, such as a mouse, trackball, stylus, or a finger on a touch screen, is used to control GUI representations. Software programs create and use their own graphical user interfaces. All internet browsers have their own graphical user interfaces that allow users to navigate across websites that may also have their own graphical user interfaces.