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!

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.

In this post, we’ll talk about some of the most popular and widely used Machine Learning libraries.

**Top Machine Learning Libraries**

**1. ****TensorFlow**

When talking of Machine Learning libraries, we must mention TensorFlow first. After all, it is undoubtedly one of the most popular Machine Learning libraries in the world. Developed by Google, TensorFlow is an open-source, JavaScript-based Machine Learning library explicitly designed for numerical computation using data flow graphs. It comes equipped with an assortment of useful tools, libraries, and resources that help in building, training, and deploying ML applications in a hassle-free manner. The best part is that it can run on GPUs, CPUs, and even on mobile computing platforms.

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.

**2. NumPy**

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.

**3. SciPy**

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.

**4. Scikit-Learn**

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.

**5. Theano**

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.

**6. PyTorch**

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.

**7. Keras**

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.

**8. Pandas**

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.

**9. Matplotlib**

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.

**Conclusion**

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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