5 tools a machine learning programmer in Python must know

We are living in the age of data, for which we need ever-increasing computer processing power and vast storage resources. Information is increasing by the day, and managing big data is becoming a real challenge. To this end, companies are building sophisticated systems and machine learning algorithms to make sense of the information and enable analysis for further processing and use. In the following article, we present five basic tools that Python experts use most often to create machine learning solutions.

Machine learning is a subset and a very important area of artificial intelligence, which deals with, among other things, building software to process data sets. With the help of advanced algorithms, computers can learn autonomously from data and past experiences to consequently predict events or make decisions without human involvement. One of the primary applications of ML today is image recognition, or the automatic understanding, generation or translation of natural language by a computer. Professionals who build and train machine learning models use a variety of languages, algorithms, techniques, patterns and frameworks to create tools to solve real-world problems. We will take a look at them.

Python – the basic programming language in the area of machine learning

The most popular in ML, continuously for many years, is Python, an open source programming language that is relatively easy to use and provides many specialized packages, such as scipy, or numpy, which have their own special applications. What’s more, Python enables rapid prototyping and improves collaboration between programmers and data analysts and data scientists (data analytics, data science). Of course, Python alone is not enough. Machine learning engineers also use a wide variety of machine learning tools and libraries when working on code.

Tools used to program ML algorithms in 2022

To create scalable and functional algorithms, machine learning developers need a good set of tools and frameworks to help them do so. There are many solutions available on the market. Below you will find TOP 5, the best options that every ML engineer should consider implementing into their daily work.

• TensorFlow – was created by the Google team in 2011, as a proprietary system dedicated to deep learning machine learning tasks. It is an open-source ML library for easily solving extremely complex mathematical problems. It allows researchers to develop experimental learning architectures and turn them into software.
• Keras – is a high-level neural network API library. Keras generates detailed and useful error messages for in-depth code troubleshooting. 
• Theano – one of the leading frameworks in the ML area. Like TensorFlow, it provides a very powerful AI library that helps optimize, define and compute complex mathematical expressions.
• PyTorch – an open source library developed by Facebook’s AI specialists. It focuses on creating neural networks, which are the basis of deep learning (deep learning). It is a library that supports the creation of commands and functionality for natural language processing platforms and applications.
• Apache Hadoop – an open source platform written in Java that contains instructions for storing and processing large amounts of data. It is also one of the fastest systems for sorting terabytes of information. In addition to performance for big data, Hadoop supports lowering the cost of server deployment.

Automate and build competitiveness with machine learning

Implementing ML and AI not only allows companies to automate simple processes and minimize the involvement of humans to handle them, but also generates savings and builds competitive advantages. To realize the full potential of machine learning, you need an experienced team that uses the best tools in their work and will be able to apply them to your project. Such is the team at Da Vinci Studio. If you are looking for an optimal solution – we are at your service.