Getting started with Python
This training has no Python prerequisites. So the first module introduces the basics of Python.
- 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
Data processing with SciPy
In data science its crucial to deal with tables: Loading, manipulating, data quality checks, etc. 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
Some pictures express more than a 1000 words. This holds in data science as well, so visualizing data is a
crucial data science skill. Matplotlib is the most popular library for this. But there are additional libraries
which build further upon this.
- Introducing the matplotlib package
- Using pyplot
- Enriching plots: Title, axis and legend
- Visualizing images
- Additional visualization packages
Machine learning introduction
Before ML can be applied the key concepts of machine learning need to be discussed.
- Which questions can machine learning answer?
- Machine learning methodology
- Data preparation
- Classes of machine learning algorithms
- Model evaluation
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
- Overview of the scikit-learn library
- 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
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
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 itâ€™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
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 5 day course Big Data Solutions on the Microsoft Azure Platform.
This course focusses on developers and data scientists who are considering the Azure stack for applying machine
learning on their data. Prior knowledge of Python or machine learning is not needed to attend this training.