I was introduced to data science when a previous employer made the decision to create a data analytics group. The initiative was designed to consolidate disparate data and use data modeling to answer specific business questions and discover organizational insights. This was exciting for me because I enjoy learning new technology and was eager to understand the infrastructure, tools, and process that would drive such an endeavor. Not knowing anything about the “field” of data science, I was enthralled at how we could use business data to make predictions and systemize those predictions for others to access.
Fast-forward about a year – I was reading an article about the predictive power of machine learning and machine learning algorithmic advances. I knew what machine learning was in concept, I didn’t have any past experience with it. As I read and learned more, the same excitement I had with the data science group a year earlier resurfaced. From there, I went on a learning-spree (more like introductory reading-spree). Although fun, I wanted a more structured way to learn about the field. I was able to connect with a machine learning expert on LinkedIn through a mutual connection. He was kind enough to spend 45 minutes talking to me about the field, its uses and provided resources to get started.
I have been working on the structured learning plan provided by the generous expert and reading everything I can. It’s only been about three months and I’m thrilled to be in the machine learning space. My next steps are to finish my six-month learning roadmap, join a local machine learning community, and compete in a Kaggle competition.
My long-term goal is to merge my software engineering background with machine learning to create “intelligent business systems”.
Thanks for following my journey!
Read more about my Machine Learning Journey.