first_post <- ‘Hello World!’

Shortly after Microsoft announced the availability of its Cognitive Services APIs in 2015 I started experimenting with services such as language understanding, computer vision, voice services. All have pre-built models and available through Web APIs to use in app development.

This picture was done using the Face APIs, try it here: Face Cognitive API

Then enter the bot framework; a framework to build conversational user experiences. I started creating bot applications that use cognitive services and integrated into the bot language understanding to detect user intent. I added advanced search capabilities. The result was a new intelligent user experience that allowed users to interact with the application through natural language, and converse with the bot using voice.

This was my introduction to the world of AI. I also started to deal with big data through my work with customers and learned a thing or two about machine learning. Now I am hooked, and I started dabbling with some statistical analytics, learned how to look at numbers differently, and the next thing I know, I enrolled to study data science.

I started my Masters in Data Science and Innovation (MDSI) at UTS in Feb 2018, and since then I completed different projects covering the different aspects of data science:

• Data acquisition and cleansing
• Data wrangling using packages such as dplyr in R and pandas in python
• Applied different predictive analytics techniques, such as linear regression, clustering, classification on data
• Applied time-series forecasting on real energy data set: data acquisition of weather observations, short-term load forecast of energy demand
• I learned how to use plotting libraries, mainly ggplot2 and matplotlib to generate graphs
• Delved deeper into data visualisations and practiced creating narratives from data using visualisations, for example In this post I took a deeper look a the NSW traffic offenses and penalties to discover some unexpected insights!
• Researched different deep learning approaches and built my first neural network from scratch.
• Built an image captioning model (and this)that combines concepts from convolutional neural networks (CNN), transfer learning and long short term memory (LSTM) networks.

The latest addition to my learning and data science portfolio is the work I did as part of the Innovation Lab subject during the Spring 2019 term. In this subject, I worked on computational materials science research, applying different skills I acquired in data wrangling and visualisation to analyse computational materials science results. More to come on this topic in a later post!

I am setting up this site to bring my blogs under one platform, here I am using github pages and jekyll.

I will use this platform to reflect on my learning journey and share public notebooks of ML/AI problems and projects I work on. I hope you enjoy it!

Mutaz