Machine Learning and Python - Why Is It a Good Combination?

In the world of business that becomes more and more data-driven, it is important to stay updated on the topic of the most popular technologies. The group of languages that are most often used for machine learning includes Python. But, is it a good choice for those interested in implementing ML-based solutions in their companies?

Machine learning-based systems already partially replace people, performing tasks that previously could only be performed by humans. Multiple jobs can be automated using artificial intelligence. There are many programming languages leveraged for data science and AI development.

According to the Refinitiv AI/ML Survey from 2020, 72% of respondents state AI/ML is a core component of their business strategy and as much as 80% claim that they are making significant investments in AI/ML technologies. Machine learning is no longer a new phenomenon in various markets all over the world.

Python's popularity among machine learning developers has been growing in recent years, and it remains one of the solutions that are most often chosen for machine learning. Python is concise and readable, enabling programmers to focus on ML-related problems rather than on the inefficiency of the language.

Python has a lot of libraries dedicated to machine learning projects and AI development, including sci-kit-learn, Tensorflow, pylearn2, NumPy, SciPy, and many others. Python is supported by many platforms including macOS, Windows, and Linux, and may be easily integrated with components written in different languages like Java, .NET, or C/C++ libraries.

Python use cases in machine learning include creating business insights for general business optimization, predictive analytics in healthcare for disease prognosis, risk assessment in the finance sector, chatbot development for customer service improvement, and recommendation systems for e-commerce.

Machine learning algorithms in Python include linear regression for analyzing continuous variables, decision trees for classification tasks, and logistic regression for predictive analysis. Many Python machine learning algorithms can help you make more data-driven decisions for your business.