Machine Learning Introduction
Before ML can be applied the key concepts of machine learning need to be discussed.
- Why using machine learning
- Supervised versus unsupervised learning
- Machine learning methodology CRISP-DM
- Data preparation
- Train, validation and test sets
- Classification, regression and clustering
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,
- AI Services
- Azure OpenAI
- Speech service
- Language service
- Computer vision
- Customizing AI services
- LAB: Setup AI services and train a custom vision model
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.
- Introducing OpenAI
- Azure OpenAI Studio
- Choosing the right Deployment
- Completion versus Chat
- Deploying models as a web app
- Creating,storing and using Embeddings
Using Azure AI Services in Python
This module shows how the Azure AI services can be used from within Python code, or using a command line
- Azure AI services API
- Using Azure AI Services CLI
- Calling Azure AI Services from Python
- LAB: Programming on top of Azure AI Services
Machine Learning with scikit-learn
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
- Machine learning specific data preprocessing: normalization, standardization, one-hot encoding
- Classification using decision trees, logistic regression and support vector machines
- Model tuning: working with hyper-parameters
- Building regression models with linear regression, SVM and Neural networks
- Unsupervised learning: Clustering
- LAB: Classification and Regression with scikit-learn
Azure Machine Learning Services
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.
- Azure ML service overview
- Create a ML service workspace
- Setting up computes and datastores
- Creating and querying experiments
- Deploying and using models
- Creating and registering images
- Deploy images as web services
- LAB: Building ML models in Azure Machine Learning
Automated Machine Learning
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.
- Building and deploying Automated ML models via the portal
- Calling Automated ML from Python
- LAB: Working with Automated ML
Azure Machine Learning Pipelines
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.
- Azure ML pipelines
- Using the Azure ML Designer
- Creating pipelines in Python
- Executing Azure ML pipelines
- LAB: Building and executing Azure ML pipelines
Getting started with Deep Learning
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.
- From Neural networks to Deep learning
- Overview of deep learning frameworks
- Getting started with the Keras framework
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.