Artificial Intelligence is a wildly abused term only good for C-level execs to showcase the company and media representive or know-it-all pundits to look smart. Machine Learning, instead, is the branch of so-called artificial intelligence that is worth further investigation because of the impact it has on software. Machine Learning is just software, problem solving and consulting. Any company and every developer should look into it much deeper than into microservices or domain-driven design. Why? Because machine learning is a breakthrough and allows you to do things that, for the most part, aren’t possible otherwise.
In two days, this workshop sets the rather ambitious goal of explaining what it is, since the very beginning, and how it can be realistically used today and for achieving what. The program is split in four parts.
Foundation of ML
+ Why Math Is Needed (and Subsequently Wy We Think that ML is only for Data Scientists)
+ Supervised and Unsupervised Learning
+ Problems and Algorithms
+ The ML Pipeline
+ Overview of the Python Ecosystem
+ Overview of the .NET (growing) Ecosystem
Algorithms of shallow learning
+ Mechanics of classification and prediction
+ Decision Trees
+ Naive Bayes
+ Theory and code of Feed-forward NN
+ Recurrent NN
+ Convolutional NN
+ Adversarial NN
End-to-end solutions with Python and ML.NET
+ Taxi fare prediction
+ Sentiment analysis
+ Agile AI
Where would you go from here? All of the demos and concept are illustrated in an on-premise perspective meaning that no magical tools and services are used or presented. Everything, whether .NET or Python, can be downloaded and installed locally. The purpose is making sense of things and break up the waterfall-style separation between data science and software development (Agile AI). A bit of math will be used to demonstrate how the “magic” of algorithms and neural networks happen and what is the ultimate point of training. The end-to-end demos will also show how to realistically organize your projects to save as much time as possible and which data processing tasks are you really going to perform so that you don’t waste time with the wrong tools.
After the workshop, your (possibly mythical) vision of machine learning will be vanished to make room for an expanded view of software development. Ultimately, machine learning is yet another (cool and rewarding) specialization of software developers. Data science? They won’t go anywhere without learning software development; and those who pick up Python admittedly do that because they don’t like software development. However, companies need integrated software and data science solutions updated in a DataDevOps style. At the moment, this is easier to reach for developers than for data scientists.
Since 2003, Dino has been the voice of Microsoft Press to Web developers and the author of many popular books on ASP.NET and software architecture. Dino wrote “Architecting Applications for the Enterprise” with fellow MVP Andrea Saltarello and “Modern Web Development” and has “Programming ASP.NET Core” in the works for 2018. When not training, Dino serves as the Digital Strategist of BaxEnergy, a software firm in the energy market.
Lunch and Coffee Breaks included in the price of the Masterclass.
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