As years progress, AI is taking up a bigger and more important role in our everyday lives. From chatbots on various websites to digital assistants and automated vehicles, artificial intelligence is becoming all the more popular.

Machine learning, as a subfield of AI, allows machines to automatically learn through past data they have collected. This is especially important for software development and can make this process a lot easier as well as more productive. That being said, here are 6 machine learning tools to help with this task.

6 Machine Learning Tools to Ease Software Development

6 Machine Learning Tools to Ease Software Development

1. Shogun

Shogun is known as an open-source machine learning software library that is written in C++. This free Python machine learning package offers users various algorithms and data structures to help solve machine learning problems.

This free tool is created initially for large-scale learning and allows users to link to other popular machine learning libraries. Being able to tackle regression problems and also support vector machines for classification, it is easily able to process even 10 million samples at once.

2. TensorFlow

Being one of the more popular JS libraries available, many machine learning enthusiasts know of TensorFlow. Developed by Google, this open-source machine learning library is very flexible and is perfect for developing applications and ML models.

By using high-level APIs such as Keras, debugging becomes easier, and it allows for immediate model iteration. Software development students who think, ā€œIā€™d like someone to do my essay paperā€ and learn coding instead of writing papers can benefit from using it. TensorFlow gives you the flexibility and freedom to use its various tools and resources to easily build the applications you need.

3. Accord.NET

Accord.NET is an ML framework written in C#. It includes a large array of image and audio libraries that can help in the development of various applications. It includes the Accord.Statistics, Accord.Math and Accord.MachineLearning functions and allows for statistical processing, pattern recognition, and other applications.

Having more than 38 kernel functions, it is perfect for strictly statistics applications. At the same time, it can make sure of its audio and video processing capabilities to tackle various problems. Lastly, it can be used on various operating systems and even comes with a mobile version.

4. Apache Mahout

Being another open-source and free project, the Apache Mahout created by the Apache Software Foundation is a very helpful framework. The reason for its creation was to simplify the work of data scientists, statisticians, and mathematicians. It achieves that by providing them with a distributed linear algebra and mathematically expressive Scala DSL framework.

It allows the people in the field to implement their algorithms in a short amount of time. They can build scalable algorithms and implement the recommendation, classification, and clustering ML techniques.

5. PyTorch

PyTorch is another free-to-use, open-source, and machine learning framework that was mainly developed by the Facebook AI Research team. It is one of the most widely known and used Machine Learning libraries written in Python. It also has a C++ interface which is considered to be faster, as well as support for C and Tensor computing.

It is a flexible and fast tool that can simplify coding and supply users with a rich set of APIs. College students who use this tool to write software-related essays can use Lets Grade It to check their essays for plagiarism before handing them in. PyTorch has been used to develop plenty of useful projects for the medical field as well as libraries for various other projects.

6. SciKitLearn

Another very extensive and useful tool for Machine Learning is SciKitLearn. This is considered one of the most useful libraries for learning in Python, and it contains implementations of many ML algorithms. This allows it to be used for statistical model building as well as data access and preparation.

Machine Learning Tools to Ease Software Development

Thanks to its great selection of clean toy data sets, it allows people with little experience in ML to get started with data analysis. Therefore, it can prove to be a very useful tool for both advanced and beginner developers alike.

Making the Most out of Every Available Tool

Each machine learning tool is written in different programming languages and has different things to offer you. The end goal is to help you develop your software models and allow you to advance in your field. If providing your customers with excellent software development practices is your goal, these tools are bound to help you out.

By going through some of the most popular ones, you will be able to see which ones are worth your time. This means that you will easily be able to choose which ones can better cover your development needs.