AI and Machine Learning Algorithms Using Python

How can you take your knowledge of machine learning (ML) concepts and how Python works within them to the next level?

This data science course will give you a strong grounding in the theories of machine learning, along with practical scenarios and experience of building, validating and deploying machine learning models.

You’ll learn basic machine learning and artificial intelligence (AI) concepts and get to understand relationships in complex data. Learn to use the Python programming language and examine how state-of-the-art machine learning algorithms are created and used in the products and services of tomorrow.

Learn the fundamentals of artificial intelligence and AI theory

What are the common concepts and theories driving AI technology today? The course will teach you core principles such as ML categories (such as supervised and unsupervised learning), the most common regression techniques, and how algorithms behave and learn in machines.

Gain hands-on experience in how to deploy machine learning models

Effectively deploying machine learning models is more of an art than science. On this course you’ll find out how to bridge the gap between IT and data science in putting a good model into production.

Discover how to use Python and Azure Notebooks to derive insights from models

Python and Azure Notebooks can be used to help you gather insights from ML models once they have been deployed. This course will show you how to collect output data, responses, request rates, failure rates and more with Python and Notebooks.

This course is ideal for anyone looking to use the principles of machine learning to lay the groundwork for artificial intelligence projects.

This course is ideal for anyone looking to use the principles of machine learning to lay the groundwork for artificial intelligence projects.

• Identified theories of machine learning
• Demonstrated the use of Python and Azure Notebooks to derive insights from models
• Applied Python programming language
• Contributed with the validation and deployment of machine learning models