This course will cover how to leverage both an algorithmic as well as a deep learning approach for building features from image data on Microsoft Azure.

This course will teach you how you can build and train your traditional machine learning models using the Databricks Machine Learning runtime and MLflow to manage the end-to-end machine learning lifecycle.

In this course, you'll learn to use TensorFlow.js to build, train, and deploy machine learning and deep learning models to power client-side and server-side applications using the JavaScript language.

Learn to build recommendation engines in Python using machine learning techniques.

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

TensorFlow is the tool of choice for building deep learning applications. In this course, you'll learn how the neurons in neural networks learn non-linear functions, and how neural networks execute operations such as regression and classification.

Learn to build simple models of market response to increase the effectiveness of your marketing plans.

This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.

Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control

Are you facing the challenge of ever-changing data when it comes to machine learning? This course will teach you how to continuously train and adapt your models, ensuring long-term effectiveness.

Dive into the world of deep learning with CNNs using TensorFlow and Keras. This course will teach you how to build and optimize CNN models for real-world applications.

This course will provide an introduction to the power, flexibility and scalability of Azure Machine Learning. You will learn to implement the data science process, to prepare data and integrate data sources for use in machine learning experiments.