Microsoft Azure ML

MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure ML

The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.

Scalable ML on Big Data using Apache Spark

Master Apache Spark's scalable machine learning techniques for optimizing performance and managing large datasets.

Building and Operating ML Solutions with Azure

The subject matter addresses the Azure ML Python SDK for the development and administration of enterprise machine learning applications, as a component of the DP-100 certification program.

Future Ready Company Specialization

Secure your organization's future in unstable markets. Acquire the knowledge and abilities necessary to acclimate and succeed in a business environment that is undergoing rapid change.

Microsoft Azure ML for Data Scientists

Discover the process of identifying machine learning model types, training and deploying predictive models using Azure Machine Learning's automated capabilities, developing regression, classification, and clustering models with Azure Machine Learning Designer, and deploying models seamlessly without scripting.

AI in Microsoft Azure

Begin your AI voyage with our comprehensive course on Microsoft Azure, which covers key AI concepts, responsible AI principles, and preparation for the AI-900 certification exam. This course is suitable for both beginners and experienced professionals.

Managing ML Projects

This second course in Duke University's AI Product Management Specialization delves into the practical aspects of managing machine learning projects, such as the identification of opportunities, the application of data science processes, the making of critical technological decisions, and the implementation of best practices from concept to production.

Gen AI: Foundation Models and Platforms

The course delves into the fundamental models and concepts of generative AI, as well as foundation models, pre-trained models for AI applications, and a variety of generative AI platforms, including IBM Watson and Hugging Face.

AI Infrastructure and Operations Fundamentals

Gain a comprehensive understanding of AI applications, concepts, technological progression, software architecture, and deployment considerations across various environments.

ML: Predict Numbers from Handwritten Digits using a Neural Network, Keras, & R

Develop and evaluate a neural network that can identify handwritten numerals, implement One Hot Encoding for classification, and evaluate the efficacy of the model through practical exercises.