What is Machine Learning with Python?

Python is a well-liked language for machine learning, and you can create machine-learning models in Python using a variety of modules and frameworks. Popular choices comprise:

1. Scikit-learn: A general-purpose machine learning package in Python called Scikit-learn offers a variety of algorithms for jobs like classification, regression, clustering, and dimensionality reduction. It is based on NumPy and SciPy and has been designed in a way to makes it simple to use and effective.
2. TensorFlow: TensorFlow is a library for machine learning and artificial intelligence developed by Google. It is designed to be flexible and efficient and is particularly well-suited for deep learning.
3. Keras: Keras is a high-level library for building and training deep learning models. It runs on top of TensorFlow, Theano, or CNTK and is designed to be easy to use and fast to prototype with.
4. PyTorch: PyTorch is a library for machine learning and deep learning that offers a dynamic computational graph, which allows you to change the structure of the model on the fly. It is particularly well-suited for building and training large, complex models.

To start with machine learning in Python, you will need to install one or more of these libraries, as well as a Python development environment. You can then follow tutorials and examples to learn how to use them to build and train machine-learning models. There are also many resources available online, such as online courses and books, that can help you learn more about machine learning with Python.