Aspiring data-science and machine-learning developers now have more Microsoft-made free video tutorials to learn how to build software in Python, one of today’s most popular and versatile programming languages.
Microsoft has released two more Python series for beginners in the form of two three-hour courses on YouTube, which add to the 44-part Python for Beginners series it released last fall.
Those short tutorials were aimed at developers with a basic level of understanding of Python and who may have previously coded in JavaScript, rather than at complete novices.
So far, the first series has been viewed 1.7 million times, suggesting it has become a popular resource for budding Pythonistas.
The new More Python for Beginners series consists of 20 videos that run between two minutes and 15 minutes each. It covers working with files, lambdas or ‘anonymous functions’, and object-oriented programming, and each tutorial is followed by a short demo video. The tutors also introduce some newer functionality to support asynchronous development through async/await.
The new series are once again presented by Christopher Harrison, a senior program manager at Microsoft, and Susan Ibach, a business development manager from Microsoft’s AI Gaming unit.
The second of the two new series, called Even More Python for Beginners: Data Tools, follows the same format and consists of 31 videos.
Harrison and Ibach say it will help students build a toolkit to get into data science and machine learning using Python. It covers the use of Jupyter Notebooks, a popular browser-based development environment, and popular data-science Python libraries.
“While we’re not going to get into conversations about choosing algorithms or building models, we are going to introduce what you’ll use when you begin the journey. We’ll highlight Jupyter Notebooks, the favorite tool of data scientists,” the pair write in a blogpost.
“We’ll introduce a couple of common libraries – NumPy and pandas – which are used to help you manage data. You’ll see how to create tables in memory, and how to load, save and manipulate data. We’ll finish by opening Scikit-learn to create a model and graph the results.”
The courses and additional material that Microsoft links to of course offer instructions on how to use Python and associated tools within the Azure cloud.
For example, the tutors suggest that students could predict flight delays by importing airline arrival data into a Jupyter notebook running on Azure Notebooks.
