Due to its simplicity, Python is considered the most popular programming language for AI development. It is very easy to understand and learn, that is why a lot of machine learning developers and data scientists prefer to use it over other languages. It is very useful when it comes to model building and analysis.

For coding beginners who are just starting to be familiar with machine learning and AI development, Python is also a good choice. It has several specific library packages that help novices get started with machine learning. For starters, the GNU Scientific Libraries NumPy and SciPy enable the computer to understand linear algebra.

Related course: Complete Machine Learning Course with Python

Below are some of the recommended packages for machine learning in Python:

1. scikit-learn

scikit-learn is the most widely used package for machine learning. It is simple, accessible for everyone’s use, and can be reusable in particular situations. It is built with efficient tools and impressive features that makes data mining and data analysis very easy, such as regression, clustering, and classification algorithms. It works with SciPy and NumPy numerical and scientific libraries.

2. Mlpy

Similar to scikit-learn, Mlpy is a machine learning library package that is built using NumPy and SciPy. It provides comprehensive machine learning methods for both supervised and unsupervised problems. It is one of the popularly used packages in for ML because it finds a reasonable compromise among maintainability, modularity, usability, reproducibility, and efficiency.

3. Nilearn

Nilearn is another Python library that can be used for advanced machine learning. Specifically designed for statistical learning of neuroimaging data, it is often used for multivariate statistical techniques, pattern recognition, predictive modelling, decoding, brain parcellations, connectomes, or functional connectivity,

4. TensorFlow

TensorFlow is an open-source Python library developed by Google for machine learning across various tasks and functions. Patterned after its predecessor, DistBelief, TensorFlow is currently being utilized for product research and development purposes.

With the objective of making it similar to how human beings learn and reason, TensorFlow has the ability to create and train neural networks to detect and decipher patterns, as well as correlations.

5. python-weka-wrapper

Built at the University of Waikato in New Zealand, Weka is a suite of machine learning programs written in Java. It comes with algorithms and tools used for predictive modeling and data analysis. It is designed with an excellent graphics user interfaces that makes it easy to access its functions.

The python-weka-wrapper package enables Weka algorithms to run smoothly and filters from within Python.

The more you try to study and use Python, the more you will understand that there are indeed several packages recommended for machine learning in Python. This is the reason why it is the top language of choice by developers who require data analysis or statistical techniques in their work. Python really shines in the field of machine learning.