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5 Best Machine Learning Frameworks of 2020

Machine learning and Artificial Intelligence have become the buzz words of today in the tech-dominated world. So much so that professionals and enthusiasts alike have started to gravitate towards the technology in order to reap the career benefits machine learning, big data, and AI brings. As far as the applications of machine learning are concerned, they too, have grown considerably as more and more businesses are adopting these futuristic technologies in their daily operations.

Take these statistics, for example, according to Statista – The total funding that was allocated to machine learning globally was $28.5 billion alone. Which is a staggering amount, and the ML market is expected to grow even more. The transition from traditional to machine learning and AI revolution has led to every software development companies, and the software industry as a whole to adopt the necessary skills and understanding required to make ML and AI-powered software solutions to cater to the growing demand.


In this light, several machine learning frameworks have been developed, which can be made use of to facilitate your business. However, it is imperative to choose the right software that meets the right requirements and give the right benefits. For this, I have jotted down some of the best machine learning frameworks of 2020.

But before diving into the details, let us have a clear mind machine learning itself.

What is Machine Learning?

If you type this very question onto Google, you will get a plethora of different definitions that speak about the same thing. If I were to define machine learning, it is at the very basic, a practice of using algorithms that can learn from data without having to be explicitly programmed. Further, machine learning is a sub-domain of AI and enables machines to behave somewhat like a human – that allows these smart machines to automate our tasks by self-learning algorithms.

This is where Deep Learning comes in, which is a subsidiary of machine learning. It optimizes the accuracy and efficiency that exceeds an average ability of a human brain. This is why data scientists are developing deep learning platforms that cater to the growing business demand for accuracy and proficiency.

Types of Machine Learning

There are three types of Machine Learning.

So what are these platforms that I speak of? They are frameworks that are either an interface or tools that remove the need to understand machine or deep learning algorithms in detail and enable the developers with the power to construct Deep Learning models easily and according to their project demand.

Machine Learning Frameworks of 2020

These frameworks allow developers to design machine learning models without having the technical knowledge of algorithms themselves. Powerful AI software birth from these frameworks, and we have discussed 5 of them for better understanding.

#1 Apache Mahout

This is a Deep Learning platform that is developed by Apache Software Foundation and runs on top of Apache Hadoop. It is an open-source and free platform that utilizes the MapReduce paradigm and operates on a distributed linear algebra framework. The scalable algorithms are there for classification, clustering, and batch-based collaborative filtering.

Mahout offers matrix and vector libraries, distributed fitness functions for programming, and much more. If we speak about the other benefits that Apache Mahout offers, then it includes the fact that it can easily handle extensive data sets, is extensible, and easy to use. However, if you are planning on using Apache Mahout, then you are looking at extensive detailed documentation for ease of use.

#2 TensorFlow

Making second in our list is TensorFlow as one of the best machine learning tools of 2020 – not only is it open-source but is a JavaScript library as well. Much like Apache Mahout, it is also a free platform with APIs that assist in the building of Machine Learning models.

TensorFlow is the creation of Google and offers extensive, flexible features, a vast programming library, and other resources for a plethora of development tasks.

The developers can run this framework in their CPUs and GPUs. It also offers support for classifications, regression, and neural networks. In addition to these, the benefits include writing algorithms for software. Further, TensorFlow can be used in two ways: A) Script Tags B) Installation through NPM.

However, not all is as easy as claimed here about Tensor Flow – the two drawbacks of this ML framework are: Not easy to learn and challenging for beginner developers.

#3 Shogun

Yet another open-source framework that is compatible with C++ programming language. This ML framework allows developers to connect with many machine learning libraries that include, LibLinear, LibSVM, SVMLight, LibOCAS, and more. However, Shogun doesn’t only support C++, but also R, Java, Octave, C#, Ruby, Lua, MatLab, Python, and more.

Speaking about its advantages, they include: Assistance in Hidden Markov implementation, ability to process large sets of data, is easy to use, and has flexible features that enable developers to easily learn and implement.

#4 Sci-Kit Learn

Sci-Kit Learn is a free platform that supports programming in python, as it contains an entire library for Python development. It specializes in data mining and data analysis. Moreover, this ML framework is best for designing models and algorithms for classifications, regression, clustering, pre-processing Dimensional reduction, and Model selection.

Fresher developers can easily understand this framework, and with its detailed documentation that is available, it adds more ease to the understanding process.

#5 H20

It is an AI tool that specializes in the decision-making process based on the data sets available. This tool is open-source and readily available for businesses looking to analyze their data and make informed decisions. Another thing, this platform greatly assists in risk and fraud analysis, insurance analytics, predictive modeling, healthcare, advertising, and customer intelligence. So countless organizations in these fields can reap better decision making benefits from this platform.

Summing Up

These were just some of the many great ML frameworks available out there for you to make use of cutting edge and futuristic technology. However, your choice of ML framework depends on your algorithm requirements, clients’ budgets, and your expertise. These free of cost Machine Learning/Deep Learning Frameworks are the technology of future furthering the field of data science.

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