Data Science with Python on Azure AI

Machine Learning Introduction

Before ML can be applied, the key concepts of machine learning need to be discussed.

• Why use Machine Learning?
• Supervised versus Unsupervised Learning
• Machine Learning Methodology CRISP-DM
• Data Preparation
• Training, Validation and Test sets
• Classification, Regression and Clustering
• AI in the Microsoft Stack
• Compare Azure ML services with Microsoft Synapse Fabric

Azure AI Services

Pre-trained 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 Azure AI services (formerly called Azure Cognitive Services) in Azure, split up in domains such as vision, speech, decision, etc. Via the Azure AI Studio, the models can be explored and fine-tuned. The Azure AI Studio also provides a first Python code snippet to continue the exploration in Python.

• Using Azure AI Studio
• OpenAI versus Azure OpenAI Service
• Azure AI Speech
• Azure AI Language
• Azure AI Vision
• Customizing Azure AI Services
• LAB: Setup and Use Azure AI Services

Azure OpenAI Service

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 Service, integrating it with the Azure platform.

• Introducing Azure OpenAI Services
• Using the Tokenizer
• Choosing the right Deployment
• Test deployment in the Azure AI Studio Playground
• 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 interface (CLI).

• Azure AI services API
• Using Azure AI Services CLI
• Calling Azure AI Services from Python
• LAB: Programming on top of Azure AI Services

Build your own Machine Learning Model 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 inspection.

• Configuring your Environment
• Machine Learning in Azure Machine Learning and Microsoft Fabric
• Using Microsoft Synapse Fabric for ML
• Normalization, Standardization, One-Hot Encoding
• Classification
• Decision Trees, Logistic Regression and Support Vector Machines
• Model tuning: Working with Hyper-Parameters
• Regression Models
• Linear Regression and Neural networks
• Unsupervised learning: Clustering
• LAB: Classification and Regression with scikit-learn

Azure Machine Learning

Machine learning on a local machine and a small dataset is one thing, running this on larger datasets or more CPU-hungry techniques (or even GPUs) 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 helps answering these questions.

• Azure Machine Learning overview
• Create an Azure Machine Learning Workspace
• Setting up Compute and Datastores
• Creating and Querying Jobs and 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
• 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 Machine Learning portal.

• Azure Machine Learning pipelines
• Using the Azure Machine Learning Designer
• Creating pipelines in Python
• Executing Azure Machine Learning pipelines
• LAB: Building and executing Azure Machine Learning 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