Creating a new notebook Once you have Jupyter installed, you can begin training your machine learning algorithm. Start by creating a new notebook. To create a new notebook, select the folder where you ...

In this online data science specialization, you will apply machine learning algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Beginning ...

Scikit-learn is a library with many uses, such as for classical machine learning algorithms, like those for spam detection, image recognition, prognostication, and customer segmentation.

Machine learning: Python's code can implement machine learning, which helps refine algorithm-based tech from voice recognition to content recommendation.

In most discussions, deep learning means using deep neural networks. There are, however, a few algorithms that implement deep learning using other kinds of hidden layers besides neural networks.

Machine learning is hard. Algorithms in a particular use case often either don't work or don't work well enough, leading to some serious debugging. And finding the perfect algorithm–the set of ...

As a Python library for machine learning, with deliberately limited scope, Scikit-learn is very good. It has a wide assortment of well-established algorithms, with integrated graphics.

Faster machine learning with scikit-learn key algorithms accelerated with Intel DAAL TensorFlow* and Caffe* libraries optimized for Intel architecture (now available on the Intel channel at ...