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Before ML can be applied the key concepts of machine learning need to be discussed.
This training has no Python prerequisites. So first the basics of Python are covered: iteration, data structures, functions, classes and libraries
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
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.
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.
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 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.
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.
Machine learning looks for patterns in data, to help understand the past or to predict future outcomes. Data is the new gold. But just a gold needs processing to make it pure and beautiful, also data needs to be extracted, cleansed and prepared before machine learning can be applied to it. A popular language for data processing and machine learning is Python. This training has 3 important learning goals. A first goal is to learn the fundamentals of machine learning: Terminology and methodology. A second goal is to learn the Python language as a tool to load and cleanse data, and apply machine learning on this data. The final goal is to learn how Azure Machine Learning can be used to manage and scale up the machine learning process in the Microsoft cloud.
This course focusses on developers and data scientists who are start using machine learning on their own infrastructure or 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, but some basic coding skills are handy.