Data Science with Python on the Microsoft Azure Platform

Machine learning introduction

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

Getting started with Python

This training has no Python prerequisites. So first the basics of Python are covered: iteration, data structures, functions, classes and libraries

• Introducing the Python programming language
• Python environments
• Interactive development in Jupyter notebooks
• Variables and objects
• Looping and iteration
• Common data structures: Lists, tuples, sets and dictionaries
• LAB: Coding in Python

Developing modular Python code

To build code that remains readable and maintainable it is important to be able to break up code in reusable components such as functions and classes

• Functions
• Creating classes
• Using classes
• Inheritance
• LAB: working with functions and classes in Python

Data processing with SciPy

In data science its crucial to deal with tables: Loading, manipulating, data quality checks, … 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
• LAB: Loading and manipulating datasets in 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, and are easier to use. This module explains different ways to visualize data in Python.

• Plotting with pandas
• Introducing the matplotlib package
• Using the seaborn package
• Creating interactive plots with Plotly
• LAB: Plotting data in Python

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: 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
• 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

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