I am pleased to report that I have just passed another Microsoft course, this time from the Microsoft Professional Program for Artificial Intelligence:
Introduction to Artificial Intelligence, with a final mark of 100%.
This was a fascinating course, providing a very good introduction to machine learning, text analysis, computer vision (including face recognition and video analysis) and conversation as a platform (chatbots and Natural Language Processing [NLP]).
Adding a chatbot to your organisation’s website can provide a more interactive experience for your users while at the same time reducing demands on your staff’s time. Chatbots can help to:
- free your team to deal with more complex enquiries or tasks
- speed up employee training by providing a very accessible and intuitive source for staff to obtain information internally
- automate complex workflows (such as providing quotes or booking services)
- provide availability 24/7, 365 days a year
- provide an alternative user interface for your apps than the traditional point and click menu/button system
As a “proper” programmer, used to programming in heavy duty, compiled languages like C# (and before that C++ and C), my reaction on discovering during my Data Science journey that R and Python are heavily used by data scientists was: why??
Why would anyone use an interpreted language, which is therefore bound to be slower, and why would anyone go to the trouble of using yet another language when there are perfectly good compiled languages around like C#, F# and VB.net?
The answer seems to be partly that R and Python are free (open source), and also because R and Python have excellent visualisation tools, which the other languages currently lack.
I mentioned a couple of days ago (here) that I had completed the 10 courses required for the Microsoft Professional Program for Data Science. I was delighted to receive confirmation earlier today from Microsoft via a nice certificate (see pic above), or you can view it here.
I am delighted to have now completed the Microsoft Professional Program for Data Science. It has been 10 online courses (taking a total of 322 hours) over just more than 11 months and my average (mean) mark over the 10 courses was 96.6%. The final course was a capstone project which involved analysing data from the 2015 earthquake in Nepal, building a model to help predict the degree of damage to buildings (amongst other things to help emergency response teams prioritise rescue efforts) and producing a report on this. This was an extremely practical way to complete the course.
I have created a series of slides (collected together in a Microsoft Sway online document) showing the main stages of my journey. You can see them at https://sway.com/lsUjwGITuGFpsHIM?ref=Link