Latest Articles from Janakiram MSV

Machine Learning Is Not Magic: It’s All About Math, Stats, Data, and Programming

This piece is the first in a series, called “Machine Learning Is Not Magic,” covering how to get started in machine learning, using familiar tools such as Excel, Python, Jupyter Notebooks and machine learning cloud services from Azure and Amazon Web Services. Check back here each Friday for future installments. 

Back in 2010, when I first encountered the concept of Machine Learning (ML), I told myself that it is only for PhDs in Computer Science, which meant that I might never get a chance to work on it.

As an ex-Microsoftie and Azure enthusiast, I decided to take a closer look at ML when Microsoft started to add Machine Learning components to Azure. Even then, I only got overwhelmed and confused by the enormous number of technologies and jargons surrounding it. With Google announcing TensorFlowand Cloud ML followed by Amazon’s launch of its own Machine Learning service, it started to become very clear that ML is going to be the next big thing in the cloud.

Looking at the buzz and the hype around ML, I decided to write my first “Hello World” equivalent of Machine Learning. With each attempt, I was only left more confused and disappointed. The sheer number of articles, blogs, self-learning courses, tutorials, and samples on ML added to my anxiety. Despite all the available resources, I couldn’t even get close to creating a meaningful and complete ML implementation.

One of the main reasons why I kept making a U-turn was the liberal dosage of mathematics found in almost every ML resource that I bookmarked. Despite my determination and commitment, the thought that I need to learn advanced mathematics kept pushing me away. Let me admit it — I dread dealing with mathematics. I barely managed to pass my math papers in high school. When I was a teen, I rejoiced when I found that it was possible to build a career in IT without a master’s degree in mathematics. The fact that some advanced math became a prerequisite for ML disappointed me and, in many ways, brought back the nightmare of my school days.

But as I continued to work with my customers on Internet of Things and data-centric projects, the possible usage of ML kept coming back to us. Meanwhile, the hype around ML has reached the peak. So much so that the cloud providers started to push ML more than the core IaaS components like VMs, storage, and networking. It also became extremely clear that ML is becoming the front and center of many emerging technologies including Cognitive Computing, Artificial IntelligenceChatbots, Personal Assistants, and Predictive Maintenance.

Read the entire article at The New Stack

is an analyst, advisor, and architect. Follow him on Twitter,  Facebook and LinkedIn.

Janakiram MSVMachine Learning Is Not Magic: It’s All About Math, Stats, Data, and Programming

Related Posts