Explore best practices for building enterprise-level applications using Spring Boot, focusing on scalability and modular design.

Learning how to get started with Blazor is only the first step. This course will take you further to the level of getting started on realistic Blazor server web applications.

Kafka can do more than just storing streaming data. This course will teach you to aggregate and analyze data in Kafka with ksqlDB and Kafka Streams.

In this course, we will learn how to easily design and build complex solutions based on the Event-Driven Architecture in the Azure Cloud.

In this course, we will learn how to easily design and build complex solutions based on the Event-Driven Architecture in the Azure Cloud.

Discover how to build fault-tolerant microservices in Java using the @Fallback annotation. Learn to create resilient services that handle failures gracefully, using fallback methods to ensure high availability and minimize service disruption in case of errors.

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 covers conceptual and practical aspects of pre-processing images to maximize the efficacy of image processing algorithms, as well as implementing feature extraction, dimensionality reduction, and latent factor identification.

Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.

This course covers various techniques for encoding categorical data, starting with the familiar forms of one-hot and label encoding, before moving to contrast coding schemes such as simple coding, Helmert coding, and orthogonal polynomial coding.

This course exhaustively covers data preprocessing techniques and transforms available in scikit-learn, allowing the construction of highly optimized features that are scaled, normalized and transformed in mathematically sound ways to fully harness the power of machine learning techniques.

This course covers aspects of extracting information from text documents and constructing classification models including feature vectorization, locality-sensitive hashing, stopword removal, lemmatization, and more from natural language processing.