In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Transfer learning is one of the core concepts leveraged for building Generative AI applications. This course teaches the essentials of transfer learning as well as advanced strategies such as fine-tuning and feature extraction.

This course will teach you neural network troubleshooting and performance tuning from a data scientist's perspective.

Extend your knowledge of the core techniques of computational linguistics by working through case-studies and visualizing their results.

Do you want to learn how machines can learn tasks we thought only human brains could perform? Then take this Deep Learning course developed by IVADO, Mila and Université de Montréal: an extensive overview of the essentials of deep learning, this ground-breaking technology already prevalent in our lives and spanning all sectors.

In the last six years, the financial sector has seen an increase in the use of machine learning models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.

Reinforcement learning is a type of machine learning which allows decision makers to operate in an unknown environment. In the world of self-driving cars and exploring robots, RL is an important field of study for any student of machine learning.

An introduction to machine learning with no coding involved.

Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.

Explore the techniques of AI communication by developing speech-to-text models using TensorFlow and PyTorch. This course will teach you the essential techniques to build advanced speech-to-text models, turning spoken words into actionable commands.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.