Machine Learning
Python is used to implement machine learning models and systems. In the context of AI development, Python’s simplicity is a major plus.
Its clarity and succinct structure allows machine learning engineers to focus on the content of ML problems over writing code, which speeds up development. With Python, machine learning engineers can quickly test algorithms prior to deployment. Machine learning engineers also use a variety of Python frameworks and libraries, including:
Matplotlib and Seaborn. Machine learning engineers frequently need to execute exploratory data analysis to evaluate which algorithm to apply to a data set. These Python libraries help machine learning engineers visualize and identify trends in data.
Pandas. Machine learning engineers use this library for data manipulation and analysis. Data fuels machine learning, and every machine learning engineer must clean, process, and transform data in order to produce high-quality insights.
Scikit-learn. This Python package helps machine learning engineers to implement supervised and unsupervised algorithms. Scikit-learn includes classification, clustering, and regression algorithms. Machine learning engineers also use this tool to score algorithms for functionality and split modelling data into testing and training sets.
Keras and TensorFlow. Machine learning engineers use Keras and TensorFlow to build, train, and deploy machine learning models and deep neural networks.
Machine learning engineers rely on Python’s vast library ecosystem to manage and understand their data—and to deploy AI solutions in production.
