Intel oneAPI’s Unified Programming Model for Python Machine Learning

The popular Scikit-learn Python machine learning toolkit is a simple and powerful framework for classical machine learning. If you are training models based on linear regression, logistic regression, decision tree, or random forest algorithms, Scikit-learn is the first choice.

In classical machine learning, one is expected to perform feature engineering — identifying the right attributes — and handpicking the right algorithms aligned with the business problem. It is the right approach for most problems based on structured data stored in relational databases, spreadsheets, and flat files.

On the other hand, deep learning is a subset of machine learning that relies on large datasets and massive computational power to identify high-level features and hidden patterns in the data. When training models based on unstructured data such as images, video, and audio, deep learning techniques based on well-defined neural network architecture are preferred by ML engineers and researchers.

Read the entire article at The New Stack

Janakiram MSV is an analyst, advisor, and architect. Follow him on Twitter,  Facebook and LinkedIn.