Data Science with Python on the Microsoft Azure Platform

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
• Functions
• 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

Data inspection

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 inspection.

• 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 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