This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.

Google Cloud AI offers a wide range of machine learning services. AutoML features cutting-edge technology which uses your training data to find the best model for your use case. In this course, you'll learn to build a custom machine learning model.

Most organizations wish to harness the power of machine learning (ML) to improve their products, but they may not always have the expertise available in-house. This course shows you how to harness the power of ML for use cases using API calls.

This course will cover how to leverage Azure Machine Learning for a successful data science initiative across the key components of workflow, data pipeline, and infrastructure.

Learn to build pipelines that stand the test of time.

The course enables the engineers/data scientist to build and deploy machine learning models into the Azure cloud, just by using Azure CLI commands.

Object detection is used to locate and identify objects in images. You can use Azure AI Custom Vision to train a model to detect specific classes of object in images.

The ability for applications to detect human faces, analyze facial features and emotions, and identify individuals is a key artificial intelligence capability.

Azure Machine Learning studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure. Learn how to develop custom object detection models using this service with NVIDIA GPU accelerated virtual machines.

Azure OpenAI Service provides access to OpenAI's powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio.

Natural language processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. You can use the Language Understanding service to build language models for your applications.

Deploy an Azure Sphere device application to monitor ambient conditions for laboratory conditions. The application will monitor the room environment conditions, connect to IoT Hub, and send telemetry data from the device to the cloud. You'll control cloud-to-device communications and undertake actions as needed.