In this module, we'll explore different neural network architectures for processing natural language texts. Natural Language Processing (NLP) has experienced fast growth and advancement primarily because the performance of the language models depends on their overall ability to "understand" text and can be trained using an unsupervised technique on large text corpora. Additionally, pre-trained text models (such as BERT) simplified many NLP tasks and has dramatically improved the performance. We'll learn more about these techniques and the basics of NLP in this learning module.

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

Learn to solve real-world optimization problems using Python's SciPy and PuLP, covering everything from basic to constrained and complex optimization.

In this course you'll learn to use and present logistic regression models for making predictions.

This short course provides an introduction to IBM Process Mining.

In this module, you learn key concepts used to build machine learning models with PyTorch. You also train a neural network model that recognizes and classifies images.

Machine learning is a technique that uses large data sets to make predictions. R is an open source, statistical software that is always evolving. In this course you can learn how...

This Free Online Course Includes: 1.5-3 Hours of Learning CPD Accreditation Final Assessment

Master Amazon Redshift's SQL, data management, optimization, and security.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.