Machine Learning Introduction
Before ML can be applied, its key concepts should be discussed.
- Supervised versus Unsupervised Learning
- Machine learning methodology
- Data preparation
- Classification, regression and clustering
- Model evaluation
- Cognitive services
- Automated ML in Azure ML Services
- Working with the Azure ML Designer
Tools for Machine Learning in Azure
In this chapter we will introduce the different tools that are available to do Machine Learning in Microsoft Azure.
- Overview of Machine Learning in Azure
- Pretrained models
- Transfer Learning
- Graphical Approaches
- Coding Approaches
Getting started with Python
Since this training has no Python prerequisites, first the basics of Python are covered.
- Introducing the Python programming language
- Python environments
- Interactive development with Azure notebooks
- Variables and objects
- Common data structures: Lists, tuples, sets and dictionaries
- Creating and using classes
Getting Started with 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 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. In this chapter, the basics of Azure Machine Learning
are covered. We will discuss its components and architecture, create and connect to a workspace with Python, and see how to create compute resources.
- Why Azure Machine Learning?
- Components and Architecture of Azure Machine Learning
- Creating an Azure Machine Learning Workspace
- Setting up a Compute
- Data manipulations with Pandas
Data processing with SciPy
In data science its crucial to deal with tables: Loading, manipulating, data quality checks, â€¦ Dataframes
can help out with that, and in this module the two most important Python packages for data manipulation are
inspected: Numpy and Pandas.
- Numerical Python: Numpy
- Numpy data structures
- Pandas DataFrames
- Loading data with pandas
- Data manipulations with Pandas
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 implemented, as well as 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's and Neural networks
- Unsupervised learning: Clustering
Training and Deploying Models in Azure Machine Learning
In this chapter we will continue with Azure Machine Learning. Since the basics have already been covered, we can dive right into the Machine Learning part!
We will see how to create experiments in which we can log the results of different attempts to train a model. We will see how to train models using different computes and
pipelines. After evaluating the different models, the best model can be deployed as a webservice.
- Setting up computes and datastores
- Creating and querying experiments
- Creating and registering images
- Deploy images as web services
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 from a mathematical point of view this is 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
Data science converts data into insights by applying techniques from the field of artificial intelligence and
machine learning. This field has received a lot of attention lately, resulting in a lot of possible techniques
to tackle this problem.
In this training you will gradually dive deeper in the use of Python and the Azure stack to apply machine
learning on business data.
This training starts from data that has already been prepared and uploaded to Azure. If you are interested in
tackling this as well, consider attending our 3 day course Data Engineering on the Microsoft Azure Platform.
This course focuses on developers and data scientists who are considering the Azure stack for applying machine
learning on their data. Students should have a general background in working with data, and some experience
with business intelligence or data analysis in general. If you don't have any prior knowledge of Machine Learning, and are interested
in learning the basic concepts and the no-code Azure Solutions that are available to do Machine Learning, consider attending the 2 day course
Data Science for the Data Citizen