I have been preparing weekly for the TensorFlow Developer Certificate by taking a deep dive into an individual deep learning concept and exploring the TensorFlow applications. This week we’ll dive into Time Series Forecasting, and extremely powerful approach to predicting the future. It has many useful applications and is a very common strategy in the retail space as well as weather or production forecasting and even used by NASA searching for earth-like planets!

By now you may be pretty familiar with my story so I will skip ahead to the good stuff, with just a brief refresher that I am now preparing for the TensorFlow Developer Certificate and really falling in love with deep learning. It has many useful applications, and one in particular is Natural Language Processing. When you combine computer vision, NLP and voice recognition, artificial intelligence is really starting to feel like the sci-fi movies that inspired us as kids. I also have a video overview on my YouTube channel Part 1 (introduction), Part 2 (optimization, predictions, and ocr).

After finishing up the Deep Learning Specialization at coursera.org I was very inspired by the power of TensorFlow and the future of deep learning. I wanted to push even further into this exciting area of machine learning and pursue the TensorFlow Developer Certificate.

One of the most exciting areas of data science is the growing field of deep learning. With an explosion of compute power combined with accessible tools such as TensorFlow, complex concepts such as computer vision, natural language processing, and time series predictions can all be approached with deep learning. Andrew Ng, the co-founder of coursera.org and founder of DeepLearning.AI, is one of the worlds leading data science researchers and educators. His team put together a great Deep Learning Specialization taking a deep dive into the inner workings of building neural networks from scratch.

For my first portfolio project outside of the BrainStation Diploma Program I looked for a challenge, but something more structured with a clear evaluation criteria to measure myself against and found this open competition @DrivenData.org. The challenge - can we predict whether people received H1N1 and seasonal flu vaccines using information they shared about their backgrounds, opinions, and health behaviors? Spoiler alert, we can do a pretty decent job with a well tuned yet simple model.

I was honored to be chosen to join the team as a Teaching Assistant after completing my own diploma program. It was a goal of mine entering the program, and I demonstrated the understanding of the material, enthusiasm, dedication, and leadership qualities BrainStation was looking for. It provided a great opportunity to continue to learn as well as provide mentorship in addition to the administrative responsibilities.

The capstone process can be a bit of a rollercoaster ride. At first, it seems a bit daunting trying to come up with an idea for a machine learning project when you are just getting introduced to the concepts. Then you get excited when you realize what may be possible and start dreaming big hoping to change the world with your very first project. Of course, some humbling occurs and you realize the challenges involved with juggling the course content, personal life, and the capstone and those big dreams get whittled down into realistic goals and future aspirations. Then you dive in and really get to work bringing it to life!

I spent the better part of two years convincing myself to pursue a data science bootcamp full-time. I was always debating the timing, if I had enough experience at my last position to build from and of course getting finances in order. Then I made the jump and left my previous position as a Production Engineer for an oil and gas company in Houston and committed 100% to this evolution just days before the first official cases of COVID-19 appeared in Wuhan and would soon rattle the world. So much for timing :)

My name is Ben Polzin and I am a passionate problem solver who always dreamed of the future and how I could help shape it. Growing up I was always curious, always trying to figure out how things worked. At amusement parks when other kids were excited by the characters and spectacle, I was enthralled by how the rollercoasters worked - looking at the gears and inner workings and coming up with ideas on how to make it even better has always come naturally to me.