An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects.

A hands-on introduction to the interplay between statistics and computation for the analysis of real data.

This course helps to understand the key concepts such as networking, storage, security, maintenance, logging and monitoring, application lifecycle, troubleshooting, API object primitives etc. It also help to grow the ability to establish basic use-cases for end users.

This course helps to understand the key concepts such as networking, storage, security, maintenance, logging and monitoring, application lifecycle, troubleshooting, API object primitives etc. It also help to grow the ability to establish basic use-cases for end users.

Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction.

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference.