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Before ML can be applied the key concepts of machine learning need to be discussed.
Pretrained models are an easy way to get started building intelligent applications. These models are pre-trained for generic tasks such as image or voice recognition, text analysis and much more. Some models can be further fine-tuned to meet specific needs (transfer learning) This module presents the different AI services (formerly called Azure Cognitive Services) in Azure, split up in domains such as vision, speech, decision, etc.
Large Language models (LLMs) such as Chat-GPT have proven to be useful in a wide variety of scenarios. By providing predefined prompts, you can configure these models to help you implement chat bots, answer questions for staff and customers, summarize documents etc. Microsoft offers this service as Azure OpenAI, integrating it with the Azure platform.
This module shows how the Azure AI services can be used from within Python code, or using a command line interface (CLI).
Many business problems can be tackled by basic machine learning techniques. In this module the most common machine learning approaches such as linear regression and random forests are used with Python. We also pay attention to model inspection.
Machine learning on a local machine and a small dataset is one thing, running this on larger datasets or more CPU-hungry techniques can become a challenge. Another problem is deploying your model: How can we easily call the resulting model from within other applications? Azure Machine Learning Services helps answering these questions.
Part of the machine learning process can be automated. Azure Automated machine learning provides a web portal and a Python API to automate data preprocessing and model training.
Machine learning pipelines allow you to build parameterized, reusable pipelines with data cleansing, model training and evaluation steps. These can be made from Python code or with a graphical designer in the Azure ML portal.
From all the machine learning techniques there is one that gets popular for more challenging problems: Multiple layers of neural networks, better known as deep learning. For problems such as image recognition, speech understanding etc. this is currently the way to go. But it's from a mathematical point of view a very challenging technique. In this module the basics of deep learning are introduced.
Machine learning looks for patterns in data, to help understand the past or to predict future outcomes. This can be done by using machine learning models that have been trained by others (such as ChatGPT) or by building your own models. In this class both options are explored.
First the core concepts of machine learning are introduced. Then we study the pre-trained models in Azure AI services, such as chat, completion, speech, vision etc. You learn how to use them from within your own Python code. Next, the focus shifts to building machine learning models from scratch with Python. First you will run code on your local machine. Then Azure Machine Learning is introduced. You learn how it can help with logging all the experiments, scaling up the model training, deploy the model, monitor the deployed model,...
This course focusses on developers and data scientists who are starting to use machine learning on their own infrastructure or who are considering the Azure stack for applying machine learning on their data. Participants must have a working knowledge of the Python programming language. If needed, attend our Python for data engineering training first.